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Grade: 12th Pass

syllabus of Mathematics And Computing Engineering (MCE) in DCE


6 years ago

Answers : (1)

Aman Bansal
592 Points

Dear Manish,

Bachelor of Technology (B.Tech.) in Mathematics and Computing

Course Syllabus (2010 Onwards)


MA101 MATHEMATICS I [3-1-0-8] Prerequistes: Nil

Systems of linear equations and their solutions; vector space Rn and its subspaces; spanning set and linear independence; matrices, inverse and determinant; range space and rank, null space and nullity, eigenvalues and eigenvectors; diagonalization of matrices; similarity; inner product, Gram-Schmidt process; vector spaces (over the field of real and complex numbers), linear transformations.

Convergence of sequences and series of real numbers; continuity of functions; differentiability, Rolles theorem, mean value theorem, Taylors theorem; power series; Riemann integration, fundamental theorem of calculus, improper integrals; application to length, area, volume and surface area of revolution.


  1. D. Poole, Linear Algebra: A Modern Introduction, 2nd Edn.,Brooks/Cole, 2005.
  2. G. B. Thomas, Jr. and R. L. Finney, Calculus and Analytic Geometry, 9th Edn., Pearson Education India, 1996..


  1. G. Strang, Linear Algebra and Its Applications, 4th Edn. Brooks/Cole India, 2006.
  2. K. Hoffman and R. Kunze, Linear Algebra, 2nd Edn., Prentice Hall India, 2004.
  3. R. G. Bartle and D. R. Sherbert, Introduction to Real Analysis, 3rd Edn., Wiley India, 2005.
  4. S. R. Ghorpade and B. V. Limaye, An Introduction to Calculus and Real Analysis, Springer India, 2006.
MA102 MATHEMATICS II [3-1-0-8] Prerequistes: Nil

Vector functions of one variable - continuity and differentiability; functions of several variables - continuity, partial derivatives, directional derivatives, gradient, differentiability, chain rule; tangent planes and normals, maxima and minima, Lagrange multiplier method; repeated and multiple integrals with applications to volume, surface area, moments of inertia, change of variables; vector fields, line and surface integrals; Greens, Gauss and Stokes theorems and their applications.

First order differential equations - exact differential equations, integrating factors, Bernoulli equations, existence and uniqueness theorem, applications; higher-order linear differential equations - solutions of homogeneous and nonhomogeneous equations, method of variation of parameters, operator method; series solutions of linear differential equations, Legendre equation and Legendre polynomials, Bessel equation and Bessel functions of first and second kinds; systems of first-order equations, phase plane, critical points, stability.

  1. G. B. Thomas, Jr. and R. L. Finney, Calculus and Analytic Geometry, 9th Edn., Pearson Education India, 1996.
  2. S. L. Ross, Differential Equations, 3rd Edn., Wiley India, 1984.


  1. T. M. Apostol, Calculus - Vol.2, 2nd Edn., Wiley India, 2003.
  2. W. E. Boyce and R. C. DiPrima, Elementary Differential Equations and Boundary Value Problems, 9th Edn., Wiley India, 2009.
  3. E. A. Coddington, An Introduction to Ordinary Differential Equations, Prentice Hall India, 1995.
  4. E. L. Ince, Ordinary Differential Equations, Dover Publications, 1958.
MA201 MATHEMATICS-III [3-1-0-8] Prerequistes: Nil

Complex numbers and elementary properties. Complex functions - limits, continuity and differentiation. Cauchy-Riemann equations. Analytic and harmonic functions. Elementary functions. Anti-derivatives and path (contour) integrals. Cauchy-Goursat Theorem. Cauchys integral formula, Moreras Theorem. Liouvilles Theorem, Fundamental Theorem of Algebra and Maximum Modulus Principle. Taylor series. Power series. Singularities and Laurent series. Cauchys Residue Theorem and applications. Mobius transformations.

First order partial differential equations; solutions of linear and nonlinear first order PDEs; classification of second-order PDEs; method of characteristics; boundary and initial value problems (Dirichlet and Neumann type) involving wave equation, heat conduction equationi, Laplaces equations and solutions by method of separation of variables (Cartesian coordinates); initial boundary value problems in non-rectangular coordinates.

Laplace and inverse Laplace transforms; properties, convolutions; solution of ODE and PDE by Laplace transform; Fourier series, Fourier integrals; Fourier transforms, sine and cosine transforms; solution of PDE by Fourier transform.


  1. J. W. Brown and R. V. Churchill, Complex Variables and Applications, 7th Ed., Mc-Graw Hill, 2004.
  2. I. N. Sneddon, Elements of Partial Differential Equations, McGraw Hill, 1957.
  3. S. L. Ross, Differential Equations, 3rd Ed., Wiley India, 1984.


  1. T. Needham, Visual Complex Analysis, Oxford University Press, 1999.
  2. J. H. Mathews and R. W. Howell, Complex Analysis for Mathematics and Engineering, 3rd Ed., Narosa,1998.
  3. S. J. Farlow, Partial Differential Equations for Scientists and Engineers, Dover Publications, 1993.
  4. R. Haberman, Elementary Applied Partial Differential equations with Fourier Series and Boundary Value Problem, 4th Ed., Prentice Hall, 1998.
MA221 DISCRETE MATHEMATICS [4-0-0-8] Prerequistes: Nil

Set theory - sets, relations, functions, countability; Logic - formulae, interpretations, methods of proof, soundness and completeness in propositional and predicate logic; Number theory - division algorithm, Euclids algorithm, fundamental theorem of arithmetic, Chinese remainder theorem, special numbers like Catalan, Fibonacci, harmonic and Stirling; Combinatorics - permutations, combinations, partitions, recurrences, generating functions; Graph Theory - paths, connectivity, subgraphs, isomorphism, trees, complete graphs, bipartite graphs, matchings, colourability, planarity, digraphs; Algebraic Structures - semigroups, groups, subgroups, homomorphisms, rings, integral domains, fields, lattices and Boolean algebras.


  1. C. L. Liu, Elements of Discrete Mathematics, 2nd Ed., Tata McGraw-Hill, 2000.
  2. R. C. Penner, Discrete Mathematics: Proof Techniques and Mathematical Structures, World Scientific, 1999.


  1. R. L. Graham, D. E. Knuth, and O. Patashnik, Concrete Mathematics, 2nd Ed., Addison-Wesley, 1994.
  2. K. H. Rosen, Discrete Mathematics and its Applications, 6th Ed., Tata McGraw-Hill, 2007.
  3. J. L. Hein, Discrete Structures, Logic, and Computability, 3rd Ed., Jones and Bartlett, 2010.
  4. N. Deo, Graph Theory, Prentice Hall of India, 1974.
  5. S. Lipschutz and M. L. Lipson, Schaums Outline of Theory and Problems of Discrete Mathematics, 2nd Ed., Tata McGraw-Hill, 1999.
  6. J. P. Tremblay and R. P. Manohar, Discrete Mathematics with Applications to Computer Science, Tata McGraw-Hill, 1997.
MA222 MODERN ALGEBRA [3-0-0-6] Prerequistes: Nil

Formal properties of integers, equivalence relations, congruences, rings, homomorphisms, ideals, integral domains, fields; Groups, homomorphisms, subgroups, cosets, Lagranges theorem , normal subgroups, quotient groups, permutation groups; Groups actions, orbits,stabilizers, Cayleys theorem, conjugacy, class equation, Sylows theorems and applications; Principal ideal domains, Euclidean domains, unique factorization domains, polynomial rings; Characteristic of a field, field extensions, algebraic extensions, separable extensions, finite fields, algebraically closed field, algebraic closure of a field.


  1. N. H. McCoy and G. J. Janusz, Introduction to Abstract Algebra, 6th Ed., Elsevier, 2005.
  2. J. A. Gallian, Contemporary Abstract Algebra, 4th Ed., Narosa, 1998.


  1. I. N. Herstein, Topics in Algebra, Wiley, 2004.
  2. J. B. Fraleigh, A First Course in Abstract Algebra, Addison Wesley, 2002.

Axiomatic construction of the theory of probability, independence, conditional probability, and basic formulae, random variables, probability distributions, functions of random variables; Standard univariate discrete and continuous distributions and their properties, mathematical expectations, moments, moment generating function, characteristic functions; Random vectors, multivariate distributions, marginal and conditional distributions, conditional expectations; Modes of convergence of sequences of random variables, laws of large numbers, central limit theorems.

Definition and classification of random processes, discrete-time Markov chains, Poisson process, continuous-time Markov chains, renewal and semi-Markov processes, stationary processes, Gaussian process, Brownian motion, filtrations and martingales, stopping times and optimal stopping.


  1. P. G. Hoel, S. C. Port and C. J. Stone, Introduction to Probability Theory, Universal Book Stall, 2000.
  2. J. Medhi, Stochastic Processes, 3rd Ed., New Age International, 2009.
  3. S. Ross, A First Course in Probability, 6th Ed., Pearson Education India, 2002.


  1. G. R. Grimmett and D. R. Stirzaker, Probability and Random Processes, Oxford University Press, 2001.
  2. W. Feller, An Introduction to Probability Theory and its Applications, Vol. 1, 3rd Ed., Wiley, 1968.
  3. K. S. Trivedi, Probability and Statistics with Reliability, Queuing, and Computer Science Applications, Wiley India, 2008.
  4. S.M. Ross, Stochastic Processes, 2nd Ed., Wiley, 1996.
  5. C. M. Grinstead and J. L. Snell, Introduction to Probability, 2nd Ed., Universities Press India, 2009.
MA224 REAL ANALYSIS [3-0-0-6] Prerequistes: Nil

Metrics and norms - metric spaces, normed vector spaces, convergence in metric spaces, completeness; Functions of several variables - differentiability, chain rule, Taylors theorem, inverse function theorem, implicit function theorem; Lebesgue measure and integral - sigma-algebra of sets, measure space, Lebesgue measure, measurable functions, Lebesgue integral, dominated convergence theorem, monotone convergence theorem, L-p spaces.


  1. J. E. Marsden and M. J. Hoffman, Elementary Classical Analysis, 2nd Ed., W. H. Freeman, 1993.
  2. M. Capinski and E. Kopp, Measure, Integral and Probability, 2nd Ed., Springer, 2007.


  1. N. L. Carothers, Real Analysis, Cambridge University Press, 2000.
  2. G. de Barra, Measure Theory and Integration, New Age International, 1981.
  3. R. C. Buck, Advanced Calculus, Waveland Press Incorporated, 2003.
MA226 MONTE CARLO SIMULATION [0-1-2-4] Prerequistes: Nil

Principles of Monte Carlo, generation of random numbers from a uniform distribution: linear congruential generators and its variations, inverse transform and acceptance-rejection methods of transformation of uniform deviates, simulation of univariate and multivariate normally distributed random variables: Box-Muller and Marsaglia methods, variance reduction techniques, generation of Brownian sample paths, quasi-Monte Carlo: Low discrepancy sequences.


  1. P. Glasserman, Monte Carlo Methods in Financial Engineering, Springer, 2004.
  2. R. U. Seydel, Tools for Computational Finance, 4th Ed., Springer, 2009.
MA252 Data Structures And Algorithms [3-0-0-6] Prerequistes: MA 221 or equivalent.

Asymtotic notation; Sorting - merge sort, heap sort, priortiy queue, quick sort, sorting in linear time, order statistics; Data structures - heap, hash tables, binary search tree, balanced trees (red-black tree, AVL tree); Algorithm design techniques - divide and conquer, dynamic programming, greedy algorithm, amortized analysis; Elementary graph algorithms, minimum spanning tree, shortest path algorithms.


  1. T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, Introduction to Algorithms, MIT Press, 2001.


  1. M. T. Goodrich and R. Tamassia, Data Structures and Algorithms in Java, Wiley, 2006.
  2. A. V. Aho and J. E. Hopcroft, Data Structures and Algorithms, Addison-Wesley, 1983.
  3. S. Sahni, Data Structures, Algorithms and Applications in C++, 2nd Ed., Universities Press, 2005.
Data Structures Lab with Object-Oriented Programming [0-1-2-4]

The tutorials will be based on object-oriented programming concepts such as classes, objects, methods, interfaces, packages, inheritance, encapsulation, and polymorphism. Programming laboratory will be set in consonance with the material covered in MA 252. This will include assignments in a programming language like C++ in GNU Linux environment.


  1. T. Budd, An Introduction to Object-Oriented Programming, Addison-Wesley, 2002.
Financial Engineering - I[3-0-0-6] Prerequistes: MA 225 or equivalent.

Overview of financial engineering, financial markets and financial instruments; Interest rates, present and future values of cash flow streams; Riskfree assets - bonds and bonds pricing, yield, duration and convexity, term structure of interest rates, spot and forward rates; Risky assets - risk-reward analysis, mean variance portfolio optimization, Markowitz model and efficient frontier, CAPM and APT; Discrete time market models ? assumptions, portfolios and trading strategies, replicating portfolios, No-arbitrage principle; Derivative securities ? forward and futures contracts, hedging strategies using futures, pricing of forward and futures contracts, interest rate futures, swaps;General properties of options, trading strategies involving options; Binomial model, risk neutral probabilities, martingales, valuation of European contingent claims, Cox-Ross-Rubinstein (CRR) formula, American options in binomial model, Black-Scholes formula derived as a continuous-time limit; Options on stock indices, currencies and futures, overview of exotic options.


  1. M. Capinski and T. Zastawniak, Mathematics for Finance: An Introduction to Financial Engineering, 2nd Ed., Springer, 2010.
  2. J. C. Hull, Options, Futures and Other Derivatives, 8th Ed., Pearson India/Prentice Hall, 2011.


  1. J. Cvitanic and F. Zapatero, Introduction to the Economics and Mathematics of Financial Markets, Prentice-Hall of India, 2007.
  2. S. Roman, Introduction to the Mathematics of Finance: From Risk Management to Options Pricing, Springer India, 2004.
  3. S. R. Pliska, Introduction to Mathematical Finance: Discrete Time Models, Blackwell, 1997.
  4. S. N. Neftci, Principles of Financial Engineering, 2nd ed., Academic Press/Elsevier India, 2009.
MA322 Scientific Computing [3-0-2-8]

Errors; Iterative methods for nonlinear equations; Polynomial interpolation, spline interpolations; Numerical integration based on interpolation, quadrature methods, Gaussian quadrature; Initial value problems for ordinary differential equations - Euler method, Runge-Kutta methods, multi-step methods, predictor-corrector method, stability and convergence analysis; Finite difference schemes for partial differential equations - Explicit and implicit schemes; Consistency, stability and convergence; Stability analysis (matrix method and von Neumann method), Lax equivalence theorem; Finite difference schemes for initial and boundary value problems (FTCS, Backward Euler and Crank-Nicolson schemes, ADI methods, Lax Wendroff method, upwind scheme).


  1. D. Kincaid and W. Cheney, Numerical Analysis: Mathematics of Scientific Computing, 3rd Ed., AMS, 2002.
  2. G. D. Smith, Numerical Solutions of Partial Differential Equations, 3rd Ed., Calrendorn Press, 1985.


  1. K. E. Atkinson, An Introduction to Numerical Analysis, Wiley, 1989.
  2. S. D. Conte and C. de Boor, Elementary Numerical Analysis - An Algorithmic Approach, McGraw-Hill, 1981.
  3. R. Mitchell and S. D. F. Griffiths, The Finite Difference Methods in Partial Differential Equations, Wiley, 1980.
MA372 STOCHASTIC CALCULUS FOR FINANCE [3 0 0 6] Prerequisites: MA224, MA371 or equivalent

General probability spaces, filtrations, conditional expectations, martingales and stopping times, Markov processes; Brownian motion and its properties; Itôintegral and its extension t classes of integrands, isometry and martingale properties of Itôintegral, Itôocesses, Itôeblin formula; Derivation of the Black-Scholes-Merton differential equation, Black-Scholes-Merton formula, the Greeks, put-call parity, multi-variable stochastic calculus; Risk-neutral valuation ? risk-neutral measure, Girsanovs theorem for change of measure, martingale representation theorems, representation of Brownian martingales, the fundamental theorems of asset pricing; Stochastic differential equations, existence and uniqueness of solutions, Feynman-Kac formula and its applications.


  1. S. Shreve, Stochastic Calculus for Finance, Vol. 2, Springer India, 2004.
  2. M. Baxter and A. Rennie, Financial Calculus, Cambridge University Press, 1996.


  1. S. Shreve, Stochastic Calculus for Finance, Vol. 1, Springer India, 2004.
  2. A. Etheridge, A Course in Financial Calculus, Cambridge University Press, 2003.
  3. J. M. Steele, Stochastic Calculus and Financial Applications, Springer, 2001
  4. T. Bjork, Arbitrage theory in Continuous Time, Oxford University Press, 1999.
  5. R. J. Elliott and P. E. Kopp, Mathematics of Financial Markets, Springer, 1999.
  6. D. Lamberton and B. Lapeyre, Introduction to Stochastic Calculus Applied to Finance, Chapmans and Hall/CRC, 2000.
MA321 OPTIMIZATION [3 0 0 6] Prerequisites: Nil

Classification and general theory of optimization; Linear programming (LP): formulation and geometric ideas, simplex and revised simplex methods, duality and sensitivity, interior-point methods for LP problems, transportation, assignment, and integer programming problems; Nonlinear optimization, method of Lagrange multipliers, Karush-Kuhn-Tucker theory, numerical methods for nonlinear optimization, convex optimization, quadratic optimization; Dynamic programming; Optimization models and tools in finance.


  1. D. G. Luenberger and Y. Ye, Linear and Nonlinear Programming, 3rd Ed., Springer India, 2008.
  2. N. S. Kambo, Mathematical Programming Techniques, East-West Press, 1997.


  1. E. K. P. Chong and S. H. Zak, An Introduction to Optimization, 2nd Ed., Wiley India, 2001.
  2. M. S. Bazarra, H. D. Sherali and C. M. Shetty, Nonlinear Programming Theory and Algorithms, 3rd Ed., Wiley India, 2006.
  3. S. A. Zenios (ed.), Financial Optimization, Cambridge University Press, 2002.
  4. K. G. Murty, Linear Programming, Wiley, 1983.
  5. D. Gale, The Theory of Linear Economic Models, The University of Chicago Press, 1989.

MA351 Formal Languages & Automata Theory [3-0-0-6] Prerequisites: MA 221 or equivalent

Alphabets, languages, grammars; Finite automata, regular languages, regular expressions; Contextfree languages, pushdown automata, DCFLs; Context sensitive languages, linear bounded automata; Turing machines, recursively enumerable languages; Operations on formal languages and their properties; Chomsky hierarchy; Decision questions on languages.


  1. J. E. Hopcroft and J. D. Ullman, Introduction to Automata Theory, Languages and Computation, Narosa, 1979.


  1. M. Sipser, Introduction to the Theory of Computation, Thomson, 2004.
  2. H. R. Lewis and C. H. Papadimitriou, Elements of the Theory of Computation,Pearson Education Asia, 2001.
  3. D. C. Kozen, Automata and Computability, Springer-Verlag, 1997.
MA373 Financial Engineering - II [3 0 0 6] Prerequisites: MA372 or equivalent

Continuous time financial market models, Black-Scholes-Merton model, Black-Scholes PDE and formulas, risk-neutral valuation, change of numeraire, pricing and hedging of contingent claims, Greeks, implied volatility, volatility smile; Options on futures, European, American and Exotic options; Incomplete markets, market models with stochastic volatility, pricing and hedging in incomplete markets; Bond markets, term-structures of interest rates, bond pricing; Short rate models, martingale models for short rate (Vasicek, Ho-Lee, Cox-Ingersoll-Ross and Hull-White models), multifactor models; Forward rate models, Heath-Jarrow-Morton framework, pricing and hedging under short rate and forward rate models, swaps and caps; LIBOR and swap market models, caps, swaps, swaptions, calibration and simulation.


  1. T. Bjork, Arbitrage Theory in Continuous Time, 3rd Ed., Oxford University Press, 2003.
  2. J. C. Hull, Options, Futures and Other Derivatives, 8th Ed., Pearson India/Prentice Hall, 2011.


  1. S. Shreve, Stochastic Calculus for Finance, Vol. 2, Springer India, 2004.
  2. R. A. Dana and M. Jeanblanc, Financial Markets in Continuous Time, Springer 2001.
  3. D. Brigo and F. Mercurio, Interest rate models: Theory and Practice, Springer, 2006.
  4. N. H. Bingham and R. Kiesel, Risk-Neutral Valuation, 2nd Ed., Springer, 2004.
  5. J. Cvitanic and F. Zapatero, Introduction to the Economics and Mathematics of Financial Markets, Prentice-Hall of India, 2007.
  6. M. Musiela and M. Rutkwoski, Martingale Method in Financial Modelling, 2nd Ed., Springer, 2005.
  7. P. Wilmott, Derivatives: The Theory and Practice of Financial Engineering, Wiley, 1998.
MA374 Financial Engineering Lab [0 0 3 3]

This course will focus on implementation of the financial models such as CAPM, binomial models, Black-Scholes model, interest rate models and asset pricing based on above models studied in MA 271 and MA 373. The implementation will be done using S-PLUS/MATLAB/C++.


  1. Y. Lyuu, Financial Engineering and Computation, Cambridge University Press, 2002.
  2. P. Glasserman, Monte Carlo Methods in Financial Engineering, Springer, 2004.


  1. D. Higham, Introduction to Financial Option Valuation: Mathematics, Stochastics and Computation, Cambridge University Press, 2004.
MA423 Matrix Computations [3 0 2 8]

Floating point computations, IEEE floating point arithmetic, analysis of roundoff errors; Sensitivity analysis and condition numbers; Linear systems, LU decompositions, Gaussian elimination with partial pivoting; Banded systems, positive definite systems, Cholesky decomposition - sensitivity analysis; Gram-Schmidt orthonormal process, Householder transformation, Givens rotations; QR factorization, stability of QR factorization. Solution of linear least squares problems, normal equations, singular value decomposition(SVD), polar decomposition, Moore-Penrose inverse; Rank deficient least-squares problems; Sensitivity analysis of least-squares problems; Review of canonical forms of matrices; Sensitivity of eigenvalues and eigenvectors. Reduction to Hessenberg and tridiagonal forms; Power, inverse power and Rayleigh quotient iterations; Explicit and implicit QR algorithms for symmetric and nonsymmetric matrices; Reduction to bidiagonal form; Golub- Kahan algorithm for computing SVD.


  1. D. S. Watkins, Fundamentals of Matrix Computations, 2nd Ed., John Wiley, 2002.
  2. L. N. Trefethen and D. Bau, Numerical Linear Algebra, SIAM, 1997.


  1. G. H. Golub and C. F. Van Loan, Matrix Computations, 3rd Ed., John Hopkins University Press, 1996.
  2. J. W. Demmel, Applied Numerical Linear Algebra, SIAM, 1997.
MA471 Statistical Analysis of Financial Data [3-0-2-8] Prerequistes: MA 271 or equivalent.

Introduction to statistical packages (R / S-Plus / MATLAB / SAS) and data analysis - financial data, exploratory data analysis tools, kernel density estimation; Basic estimation and testing; Random number generator and Monte Carlo samples; Financial time series analysis - AR, MA, ARMA. ARIMA, ARCH and GARCH models, identification, inference, forecasting, stochastic volatility time series models for term structure of interest rates; Linear regression ?-least squares estimation, inference, model checking; Multivariate data analysis - multivariate normal and inference, Copulae and random simulation, examples of copulae family, fitting Copulas, Monte Carlo simulation with Copulas, dimension reduction techniques, principal component analysis; Risk management - riskmetrics, quantiles, Q-Q plots, quantile estimation with Cornish-Fisher expansion, VaR, expected short fall, time-to-default modeling, extreme value theory (generalized extreme value (GEV), generalized Pareto distribution (GPD); Block Maxima, and Hill methods).


  1. R. A. Carmona, Statistical Analysis of Financial Data in S-Plus, Springer India, 2004.
  2. D. Ruppert, Statistics and Finance: An Introduction, Springer India, 2009


  1. E. Zivot and J. Wang, Modeling Financial Time Series with S-plus, 2nd Ed., Springer, 2006.
  2. P. J. Brockwell and R. A. Davis, Time Series: Theory and Methods, 2nd Ed., Springer, 2009.
  3. V. K. Rohatgi and A. K. Md. E. Saleh, An Introduction to Probability and Statistics, 2nd Ed., Wiley India,2009.
  4. T. W. Anderson, An Introduction to Multivariate Statistical Analysis, 3rd Ed., Wiley India, 2009.
MA453 Theory of Computation [3 0 0 6] Prerequisites: MA351 or equivalent

Models of computation - Turing machine, RAM, ìecursive function, grammars; Undecidability - Rices theorem, Post correspondence problem, logical theories; Complexity classes - P, NP, coNP, EXP, PSPACE, L, NL, ATIME, BPP, RP, ZPP, IP.


  1. M. Sipser, Introduction to the Theory of Computation, Thomson, 2004.
  2. H. R. Lewis and C. H. Papadimitriou, Elements of the Theory of Computation, PHI, 1981.


  1. J. E. Hopcroft and J. D. Ullman, Introduction to Automata Theory, Languages and Computation, Narosa, 1979.
  2. S. Arora, and B. Barak, Computational Complexity: A Modern Approach, Cambridge University Press, 2009.
  3. C. H. Papadimitriou, Computational Complexity, Addison-Wesley Publishing Company, 1994.
  4. D. C. Kozen, Theory of Computation, Springer, 2006.
  5. D. S. Garey and G. Johnson, Computers and Intractability: A Guide to the Theory of NPCompleteness, Freeman, New York, 1979.
MA473 Computational Finance [3-0-2-8] Prerequistes: MA 373 or equivalent.

Review of financial models for option pricing and interest rate modeling, Black -Scholes PDE; Finite difference methods, Crank-Nicolson method, American option as free boundary problems, computation of American options, pricing of exotic options, upwind scheme and other methods, Lax-Wendroff method; Monte-Carlo simulation, generating sample paths, discretization of SDE, Monte-Carlo for option valuation and Greeks, Monte-Carlo for American and exotic options; Term-structure modeling, short rate models, bond prices, multifactor models; Forward rate models, implementation of Heath-Jarrow-Morton model; LIBOR market model, Volatility structure and Calibration.


  1. P.Glasserman, Monte Carlo Methods in Financial Engineering, Springer, 2004.
  2. R. U. Seydel, Tools for Computational Finance, 4th Ed., Springer, 2009.


  1. D. Higham, Introduction to Financial Option Valuation: Mathematics, Stochastics and Computation, Cambridge University Press, 2004.
  2. P. Wilmott, S. Howison and J. Dewynne, The Mathematics of Financial Derivatives, Cambridge University Press, 1997.
  3. Y. Lyuu, Financial Engineering and Computation, Cambridge University Press, 2002.


  1. MA421 Graph Theory
  2. MA422 Combinatorics
  3. MA424 Advanced Linear Algebra
  4. MA451 Parallel Computing
  5. MA452 Complexity Theory
  6. MA476 Portfolio Theory and Performance Analysis
  7. MA477 Financial Risk Management and Modelling
  8. MA503 Network Flows Algorithms
  9. MA504 Combinatorial Optimization
  10. MA505 Algebraic Coding Theory
  11. MA506 Number Theory and Cryptography
  12. MA507 Applied Fuzzy Algebra and Fuzzy Logic
  13. MA508 Cryptography
  14. MA509 Algebraic Number Theory
  15. MA516 Semantics of Programming Languages
  16. MA517 Functional and Logic Programming
  17. MA523 Computer Algebra
  18. MA544 Wavelets and Applications
  19. MA545 Complex Dynamics and Fractals
  20. MA546 Differential Algebraic Equations
  21. MA548 Fourier Theory
  22. MA549 Topology
  23. MA550 Measure Theory
  24. MA561 Fluid Dynamics
  25. MA562 Mathematical Modelling and Numerical Simulation
  26. MA563 Calculus of Variations and Optimal Control
  27. MA575 Mathematical Methods
  28. MA576 Large Scale Scientific Computation
  29. MA577 Perturbation Methods
  30. MA578 Computational Fluid Dynamics
  31. MA593 Statistical Methods and Times Series Analysis
  32. MA594 Statistical Decision Theory
  33. MA595 Stochastic Programming and Applications
  34. MA596 Stochastic Calculus for Finance
  35. MA597 Queueing Theory and Applications
  36. MA598 Mathematics of Financial Derivatives
  37. MA611 Differential Geometry of Curves and Surfaces
  38. MA612 Logic Programming
  39. MA614 Applications of Computational Geometry
  40. MA621 Rings and Modules
  41. MA622 Galois Theory
  42. MA623 Introduction to Algebraic Geometry
  43. MA624 Introduction to Lie Algebras
  44. MA640 Topology and Calculus of Surfaces
  45. MA641 Operator Theory in Hilbert Spaces
  46. MA651 Distributed Computing
  47. MA671 Finite Element Methods for Partial Differential Equations
  48. MA681 Applied Stochastic Processes
  49. MA682 Statistical Inference
  50. MA691 Statistical Simulation and Data Analysis

MA424 Advanced Linear Algebra L-T-P-C :3-0-0-6

Review of vector spaces, bases and dimensions, direct sums; Linear transformations, ranknullity theorem, matrix representation of linear transformations, trace and determinant; Eigenvalues and eigenvectors, invariant subspaces, upper-triangular matrices, invariant subspaces on real vector spaces, generalized eigenvectors, characteristic and minimal polynomials, triangulation, diagonalization, Jordan canonical form; Norms and innerproducts, orthonormal bases, orthogonal projections, linear functional and adjoints, selfadjoint and normal operators, Schur decomposition, spectral theorems for selfadjoint, unitary and normal operators, positive definite operators, isometry, polar and singular value decompositions.


  1. S. Axler, Linear Algebra Done Right, 2nd edition, Springer-Verlag, 1997.
  2. K. Hoffman and R. Kunze, Linear Algebra, Pearson Education India, 2003.


MA476 Portfolio Theory and Performance Analysis [3-0-0-6]

Mean-variance portfolio theory, asset return, portfolio mean and variance, Markowitz model, efficient frontier calculation algorithm, single-index and multi-index models; Capital Asset Pricing Model (CAPM), Capital market line, pricing model, security market line, systematic and nonsystematic risk, pricing formulas, investment implications, empirical tests, performance evaluation; Multifactor models, CAPM as a factor model, arbitrage pricing theory (APT), multifactor models in continuous time, data statistics, estimation of parameters; Utility functions, risk aversion, utility functions and the mean-variance criterion, linear pricing, portfolio choice, risk neutral pricing; Optimal portfolio growth, continuous-time growth, log-optimal pricing and the Black-Scholes equation; Multiperiod securities, risk neutral pricing, buying price analysis, continuous time evaluation; Fixed Income Security investment, modeling yield curves, managing a bond portfolio, performance analysis.


  1. D. G. Luenberger, Investment Sciences, Oxford Univ. Press, 1998.
  2. J.Cvitanic and F. Zapatero, Introduction to the Economics and Mathematics of Financial Markets, Prentice-Hall of India, 2007.
  3. N. Amenc and V. Le Sourd, Portfolio Theory and Performance Analysis, John Wiley & Sons, 2003.


MA451 Parallel Computing [3-0-0-6]Prerequistes: MA353 (Design and analysis of algorithms), MA352 (Theory of computation)

Scope of Parallel Computing - limits to parallelizability, NC-reductions, P-completeness; parallel programming platforms; parallel algorithm design - decomposition, task and ineractions; communication models - synchronous and asynchronous; analytical modeling of parallel programs; programming using message passing paradigm and shared address space - threads, MPI, unstructured communications; parallel algorithms and applications - matrix algorithms, sorting, graph algorithms and discrete optimization problems.


  1. A. Grama, G. Karypis, V. Kumar, A. Gupta. Introduction to parallel computing. Addison-Wesley, 2003.
  2. J. JaJa. An introduction to parallel algorithms. Addison-Wesley, 1992


  1. R. Greenlaw, H. J. Hoover, W. L. Ruzzo. Limits to parallel computation. Oxford University Press, 1995


MA421 Graph Theory [3-0-0-6]

Isomorphism, incidence and adjacency matrices, Sperner lemma, Trees, Cayley formula, connector problem, connectivity, constructing reliable communication network, Euler tours, Hamilton cycle, Chinese postman and traveling salesman problems, matchings and coverings, perfect matchings, edge colouring, Vizing Theorem, time table problem, Independent sets, Ramsey theorem, Turan theorem, Schur theorem, vertex colouring, Brook theorem, Hajos conjecture, chromatic polynomials, storage problem, planarity, dual graphs, Euler formula, Kuratowski theorem, five colour theorem, history of four colour theorem, nonhamiltonian planar graphs, planarity algorithm, directed graphs, job sequencing, one way road system, ranking participants in tournaments.


  1. J. A. Bondy and U. S. R. Murty. Graph Theory with Applications. North-Holland, 1976.
  2. J. M. Aldous. Graphs and Applications. Springer, LPE, 2007


MA422 Combinatorics [3-0-0-6]Prerequistes: Nil

Counting principles, multinomial theorem, set partitions and Striling numbers of the second kind, permutations and Stirling numbers of the first kind, number partitions, Lattice paths, Gaussian coefficients, Aztec diamonds, formal series, infinite sums and products, infinite matrices, inversion of sequences, probability generating functions, generating functions, evaluating sums, the exponential formula, more on number partitions and infinite products, Ramanujans formula, hypergeometric sums, summation by elimination, infinite sums and closed forms, recurrence for hypergeometric sums, hypergeometric series, Sieve methods, inclusion-exclusion, Mobius inversion, involution principle, Gessel-Viennot lemma, Tutte matrix-tree theorem, enumeration and patterns, Polya-Redfield theorem, cycle index, symmetries on N and R, polyominoes


  1. M. Aigner. A Course in Enumeration. Springer, GTM, 2007.


  1. C. Berge. Principles of Combinatorics. Academic Press, 1971.
  2. J. Riordan. Introduction to Combinatorial Analysis. Dover, 2002.
  3. M. Bona. Introduction to Enumerative Combinatorics. Tata McGraw Hill, 2007.


MA452 Complexity Theory [3-0-0-6]Prerequistes: MA352 or equivalent

Time and Space complexity, various complexity classes, oracle Turing machine, hierarchy theorems, relations among complexity measures, Savitchs theorem, Borodins Gap theorem, Blums speed-up theorem, the union theorem, axiomatic complexity theory, intractable problems, PSPACE-completeness, EXPSPACE-completeness, QBF problem, provably intractable problems, P = NP?, alternating time and space.


  1. J. E. Hopcroft and J. D. Ullman, Introduction to Automata Theory, Languages and Computation, Narosa, 1979.
  2. M. Sipser, Introduction to the Theory of Computation, Thomson Asia Pte Ltd., 1997.


  1. D. Bovet and P. Crescenzi, Introduction to the Theory of Complexity, Prentice Hall, 1993.
  2. D. Kozen, Theory of Computation, Springer, 2006
  3. D. S. Garey and G. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, Freeman, New York, 1979.
  4. C. H. Papadimitriou, Computational Complexity, Addison-Wesley, 1994.
  5. J. L. Balcazar, J. Diaz, J. Gabarro, Structural Complexity, 2nd Ed, Vol 1, Springer-Verlag, 1995.


MA477 Financial Risk Management and Modelling [3-0-0-6] Prerequistes: MA471 Financial Engineering -II

Basics of financial risk management, identification of major financial risks, VaR and risk management, interest rate risk, foreign exchange risk; Credit derivatives: CDS, valuation of CDS, CDO and valuations of synthetic CDO, quantitative models of credit risk; First-passage time models: Black and Cox model, dependent defaults; Hazard process: hazard function of random time, martingale hazard process; Reduced form modeling: intensity based valuation of defaultable claims; Pricing credit derivatives and portfolio optimization under credit risk.


  1. K. A. Horcher, Essentials of Financial Risk Management, John Wiley and Sons Inc, 2005.
  2. T. R. Bielecki and M. Rutkowski, Credit Risk, Modeling, Valuation and Hedging, Springer, 2002.
  3. B. Schmid, Credit Risk Pricing Models: Theory and Practice, 2nd Edition, Springer, 2004.
  4. J. C. Hull, Options, Futures and Other Derivatives, 7th Edition, Pearson Education India, 2008.
  5. C. Tapiero, Risk and Financial Management: Mathematical and Computational Methods, John Wiley and Sons, 2004
  6. J. Cvitanic and F. Zapatero, Introduction to the Economics and Mathematics of Financial Markets, Prentice-Hall of India, 2007


MA640 Topology and Calculus of Surfaces [3-0-0-6] Pre-requisites: MA 541 or equivalent

Combinatorial surface, classical Euler characteristic, Barycentric subdivision; Topological surfaces, Rados theorem on the triangulability of compact surfaces, classification theorem of compact, connected surfaces; Simplicial homology, Betti numbers, Poincares theorem on Euler characteristic; Differential surfaces, tangent spaces, vector fields, Poincare index theorem; Topological and differential manifolds.


  1. L.C. Kinsey, Topology of Surfaces, Springer, 1993.
  2. M.A. Armstrong, Basic Topology, Springer, 2010.
  3. M. Henle, A Combinatorial Introduction To Topology, Dover, 1994.


MA614 Applications of Computational Geometry [3-0-0-6] Prerequisites: MA 252 or equivalent

Basics of Computational Geometry - convex hull, line segment intersection, triangulation, linear programming, simplex range searching, voronoi diagram (nearest and farthest), arrangement and duality, visibility; Applications of geometric data structures and algorithms - geographic information system (GIS), robot motion planning, physical design in VLSI.


  1. M. de Berg, O. Chenong, M. van Kreveld and M. Overmars, Computational Geometry: Algorithms and Application, ,Springer-Verlag, 3rd Edn., 2008.
  2. F. Preparata and M. Shamos, Computational Geometry: An Introduction, Springer-Verlag, 1985.


  1. K. Mulmuley, Computational Geometry: An Introduction Through Randomized Algorithms, Prentice-Hall, 1994.
  2. H. Edelsbrunner, Algorithms in Combinatorial Geometry, Springer-Verlag, 1987.
  3. C. J. ORourke, Computational Geometry, Cambridge University Press, 1998.


MA624 Introduction to Lie Algebras [3-0-0-6] Prerequisites: MA 521 and MA 522 or equivalent.

Lie algebras and Lie algebra homomorphisms (definition and examples), solvable and nilpotent Lie algebras, Engels theorem; Semisimple Lie algebras - Lies theorem, Cartans criterion, Jordan-Chevalley decomposition, killing form, complete reducibility of representations, Weyls theorem, irreducible representations of the Lie algebra SL(2), weights and maximal vectors, root space decomposition; Root systems - definition and examples, simple roots and the Weyl group, Cartan matrix of a root system, Dynkin diagrams, classification theorem.


  1. J. E. Humphreys, Introduction to Lie Algebras and Representation theory, Graduate texts in Mathematics, Springer, 1972.
  2. W. Fulton and J. Harris, Representation theory - A First Course, Graduate texts in Mathematics, Springer, 1991.
  3. K. Erdmann and M. J. Wildon, Introduction to Lie Algebras,Springer Undergraduate Mathematics Series, 2006.
  4. B.. C. Hall, Lie Groups, Lie Algebras, and Representations, An Elementary Introduction, Graduate Texts in Mathematics, Springer, 2010.
  5. N. Jacobson, Lie Algebras, Courier Dover Publications, 1979.


MA612 Logic Programming [2-0-2-6]

Logic and reasoning; Predicate logic - terms, formulae and clauses, clausal form of formulae, types of clauses, Horn clauses, substitution, unification algorithm, resolution, SLD-refutation; Introduction to Prolog, structure of logic programs, syntax and meaning, controlling backtracking, negation in logic programs and implementation issues, lists, operators, arithmetic, input and output, built-in predicates, operations on data structures, meta-programming; Constraint logic programming.


  1. I. Bratko, Prolog: Programming for Artificial Intelligence, 3rd Edn., Pearson, 2001.
  2. M. Ben-Ari, Mathematical Logic for Computer Science, 2nd Edn., Springer, 2003.
  3. J. W. Lloyd, Foundations of Logic Programming, Springer Verlag, 1987.
  4. T. Fruhwirth, H. Wiesenthal, and S. Abdennadher, Essentials of Constraint Programming, 1st Edn.,Springer, 2003.
  5. K. R. Apt, and M. Wallace, Constraint Logic Programming Using Eclipse, Cambridge University Press, 2007.


MA691 Statistical Simulation and Data Analysis [3-0-0-6] Prerequisites: MA 471 or equivalent

Introduction to probability distributions. Basics of estimation and testing of hypothesis ( frequentist approach, bayesian approach ). Different censoring schemes: Type-I , Type-II, hybrid, progressive. Different models and EM algorithm: mixture model; bivariate distributions; cure rate model; competing risk model. Generating random sample: discrete and continuous multivariate distributions (multinomial, multivariate normal, multivariate exponential ); acceptance rejection principle; monte carlo markov chain ( metropolis hastings algorithm, gibbs sampler ); Convergence of MCMC : Harris irreducibility, recurrence, minorization, limit theory for harris recurrent markov chains. Resampling techniques: jackknife; bootstrap. Hidden Markov Model ( forward-backward algorithm, viterbi algorithm, baum-welch algorithm). Artificial Neural Network: framework, topology ( feed forward neural network, recurrent neural network), training of ANN ( supervised, unsupervised, reinforced learning ) , robustness. Genetic Algorithm: single objective GA, multi-objective NSGA.


  1. D. Kundu, Statistical Computing :existing methods and recent development, Alpha Science International, 2004.
  2. B.P. Robert and G. Casella, Monte Carlo Statistical Methods, Springer, 2004.


  1. W. Zucchini and I. L. MacDonald, Hidden Markov Model for time series: an introduction using R, CRC Press, 1999.
  2. R. C. Mitsno, Genetic Algorithm and Engineering Optimization, Wiley Series in EDA, 2000.
  3. J.E. Gentle, Elements of computational Statistics, Springer Series in Statistics and computing, 2002


MA682 Statistical Inference [3-0-0-6] Prerequisites: MA225/MA590 or equivalent

Review of probability theory; Sampling distributions; Point estimation - estimators, sufficiency, completeness, minimum variance unbiased estimation, maximum likelihood estimation, method of moments, Cramer-Rao inequality, consistency; Interval estimation; Testing of hypotheses - tests and critical regions, Neymann-Pearson lemma, uniformly most powerful tests, likelihood ratio tests; Basic non-parametric tests.


  1. G. Casella and R. L. Berger, Statistical Inference, 2nd edition, Duxbury Press, 2001.
  2. J. Shao, Mathematical Statistics, 2nd edition, Springer, 2007.
  3. V. K. Rohatgi and A. K. Md. E. Saleh, An Introduction to Probability and Statistics, 2nd edition, Wiley, 2000.
  4. B. L. S. Prakasa Rao, A First Course in Probability and Statistics, World Scientific/Cambridge University Press India, 2009.
  5. G. G. Roussas, An Introduction to Probability and Statistical Inference, Academic Press, 2002.
  6. K. L. Chung, A Course in Probability Theory, 3rd edition, Elsevier, 2001.


MA681 Applied Stochastic Processes [3-0-0-6] Prerequisites: MA225/MA590 or equivalent

Review of probability theory; Discrete-time Markov chains, renewal and regenerative processes, Poisson processes, continuous-time Markov chains, martingales, Brownian motion; Application to queues, communications, finance, biology and manufacturing.


  1. R. Serfozo, Basics of Applied Stochastic Processes, Springer, 2009.
  2. M. Lefebvre, Applied Stochastic Processes, Springer, 2007.
  3. R. F. Bass, Stochastic Processes, Cambridge University Press, 2011.
  4. S. Resnick, Adventures in Stochastic Processes, Birkhauser, 1992.
  5. A. Goswami and B.V. Rao, A Course in Applied Stochastic Processes, Hindustan Book Agency, 2006.
  6. U. N. Bhat and G. K. Miller, Elements of Applied Stochastic Processes, 3rd edition, Wiley, 2002.
  7. H. J. Tijms, A First Course in Stochastic Models, Wiley, 2003.
  8. G. R. Grimmett and D. R. Stirzaker, Probability and Random Processes, 3rd edition, Oxford University Press, 2001.
  9. S. M. Ross, Stochastic Processes, 2nd edition, Wiley, 1995.


MA641 Operator Theory in Hilbert Spaces [3-0-0-6] Prerequisites: MA543 or equivalent

Review of Hilbert spaces, orthonormal bases, weak convergence; Bounded operators on Hilbert spaces, adjoints of bounded operators, algebra of bounded operators; Orthogonal projections, isometric and unitary operators, finite rank and compact operators, Hilbert-Schmidt operators, selfadjoint and normal operators; Spectra of bounded operators, invariant and reducing subspaces; Spectral theorem for compact operators, polar and singular value decompositions, Schatten class operators; Spectral theorem for bounded selfadjoint and normal operators.


  1. J. B. Conway, A course in Functional Analysis, 2nd Edn., Springer-Verlag, 1990.
  2. J. Weidmann, Linear Operators in Hilbert Spaces, Springer-Verlag, 1980.
  3. G. Helmberg, Introduction to Spectral Theory in Hilbert Space, North-Holland, 1975.
  4. B. V. Limaye, Functional Analysis, 2nd edition, New Age International, New Delhi, 1996.


MA671 Finite Element Methods for Partial Differential Equations [3-0-0-6] Prerequisites: MA322/MA572 or equivalent

Basic concepts of finite element methods; Elements of function spaces, Lax-Milgram theorem, piecewise polynomial approximation in function spaces, Galerkin orthogonality and Cea?s lemma, Bramble-Hilbert lemma, Aubin-Nitsche duality argument; Applications to elliptic, parabolic and hyperbolic equations, a priori error estimates, variational crimes; A posteriori error analysis ? reliability, efficiency and adaptivity.


  1. C. Johnson, Numerical Solution of Partial Differential Equations by the Finite Element Method, Dover Publications, 2009.
  2. P. G. Ciarlet, The Finite Element Method for Elliptic Problems, North-Holland, 1978.
  3. J. N. Reddy, An Introduction to Finite Element Method, McGraw Hill, 1993.
  4. S. C. Brenner and L. R. Scott, The Mathematical Theory of Finite Element Methods, 2nd edition, Springer, 2002.
  5. Z. Chen, Finite Element Methods and Their Applications, Springer, 2005.
  6. D. L. Logan, A First Course in the Finite Element Method, 4th edition, Cenegage Learning India Pvt Ltd, 2007.
  7. A. J. Davies, The Finite Element Method: An Introduction with Partial Differential Equations, Oxford University Press, 2011.


MA503 Network Flows Algorithms [3-0-0-6] Prerequisites: MA501 Discrete Mathematics (Graph theory part), MA591 Optimization Techniques

Graph notations and computer representations, Applications to various disciplines, Worst-case complexity. Shortest paths, Label setting and label correcting algorithms, Maximum flows, Augmenting path and pre flow push algorithms, Minimum cost flows. Pseudopolynomial and polynomial time algorithms, Assignments and matching, Bipartite and nonbipartite matchings, Minimum spanning trees, Convex cost flows and generalized flows, Emphasis on real-life time applications of network flows and state-of-the art algorithms.


  1. R. K. Ahuja, T. L. Magnanti and J. B. Orlin, Network Flows: Theory Applications and Algorithms, Prentice Hall, 1993.
  2. L.R. Ford and D.R. Fulkerson, Flows in Networks, Princeton University Press, 1962.
  3. A. Dolan, Networks and Algorithms, John Wiley and Sons, 1993.
  4. M.N.S. Swamy and K. Thulasiraman, Graphs, Networks and Algorithms, Wiley, 1981.


MA504 Combinatorial Optimization [3-0-0-6] Prerequisites: MA591 Optimization Techniques

Optimization problems, Convex sets and convex functions, Important combinatorial optimization problems, The fundamental algorithms, efficiency and the digital computer. Convex hulls, Polytopes, Facets, Integral polytopes, Total Unimodularity, Total dual integrality, Cutting plane algorithms and bounds, Separation and optimization, Computational complexity. Matroids, Greedy algorithm, Properties, Axioms and constructions of matroids, Matroid Intersection problem, Applications of matroid intersection, Weighted matroid intersection. Heuristics and analysis of heuristics, Heuristics for TSP, Data structure for combinatorial optimization problems.


  1. R. K. Ahuja, T. L. Magnanti and J. B. Orlin, Network Flows: Theory Applications and Algorithms, Prentice Hall, 1993.
  2. G. L. Nemhauser and L.A. Wolsey, Integer and Combinatorial Optimization, John Wiley, 1988.
  3. L. R. Foulds, Combinatorial Optimization for Undergraduates, UTM, Springer Verlag, 1984.
  4. W. J. Cook, et. al, Combinatorial Optimization, John Wiley and Sons, 1998.


MA505 Algebraic Coding Theory [3-0-0-6] Prerequisites: MA521 Modern Algebra

Binary group codes, Hamming codes, Polynomial codes, Block codes, Linear codes,Generator and check matrices, Sphere packing, Gilbert-Varshamov and Griesmer bounds, Syndrome decoding. The structure of cyclic codes, Reed Mueller codes, Simplex codes. Nonlinear codes, Golay, Hadamard, Justeen, Kerdock, Nordstorm-Robinson codes. Weight distribution of codes, Generalized BCH codes (including the BCH bound and decoding methods), Self-dual codes and invariant theory, MacWilliams identities and Gleasons theorems on self-dual codes, Covering radius problem, Convolutional codes. Reed-Solomon codes, Quadratic-residue codes and perfect codes. The group of a code, permutation and monomial groups, Mathieu groups, General linear and affine groups, Connections with design theory, Steiner systems, Projective and affine planes.


  1. Hill, A First Course in Coding Theory, Oxford University Press, 1989.
  2. V. Pless, Introduction to the Theory of Error-Correcting Codes, 3rd edition, John Wiley, 1998.


MA506 Number Theory and Cryptography L-T-P-C:3-0-0-6 Prerequisites: Nil

Congruence, Chinese Remainder Theorem, Primitive Roots, Quadratic reciprocity, Finite fields, Arithmetic functions Primality Testing and factorization algorithms, Pseudo-primes, Fermat?s pseudo-primes, Pollard?s rho method for factorization, Continued fractions, Continued fraction method Hash Functions, Public Key cryptography, Diffie-Hellmann key exchange, Discrete logarithm-based crypto-systems, RSA crypto-system, Signature Schemes, Digital signature standard, RSA Signature schemes, Knapsack problem. Introduction to elliptic curves, Group structure, Rational points on elliptic curves, Elliptic Curve Cryptography. Applications in cryptography and factorization, Known attacks.


  1. N. Koblitz, A Course in Number Theory and Cryptography, Springer 2006.
  2. I. Niven, H.S. Zuckerman, H.L. Montgomery, An Introduction to theory of numbers, Wiley, 2006.
  3. L. C. Washington, Elliptic curves: number theory and cryptography, Chapman & Hall/CRC, 2003.


  1. J. Silverman and J. Tate, Rational Points on Elliptic Curves, Springer-Verlag, 2005.
  2. D. Hankerson, A. Menezes and S. Vanstone, Guide to elliptic curve cryptography, Springer-Verlag, 2004.
  3. J. Pipher, J. Hoffstein and J. H. Silverman , An Introduction to Mathematical Cryptography, Springer-Verlag, 2008.
  4. G.A. Jones and J.M. Jones, Elementary Number Theory, Springer-Verlag, 1998.
  5. R.A. Mollin, An Introduction to Cryptography, Chapman & Hall, 2001.


MA507 Applied Fuzzy Algebra and Fuzzy Logic L-T-P-C:3-0-0-6 Prerequisites: Nil

Interval numbers, Interval arithmetic, Multilevel interval numbers. Fuzzy numbers, Fuzzy numbers in the set of integers, Arithmetic with fuzzy numbers. Definition of fuzzy sets, Fuzzy sets and fuzzy numbers, Basic operations on fuzzy sets, Extension principle of fuzzy sets. Fuzzy relations, Basic properties of fuzzy relations, Fuzzy relations and approximate reasoning. Fuzzy logic, Linguistic variables, Linguistic modifiers, Truth, Propositions of fuzzy logic, Uncertainty based information, Approximate reasoning. Fuzzy decision making, Multicriteria decision making, Multistage decision making, Fuzzy ranking methods. Fuzzy modeling of control parameters, Washing machine, Fuzzy logic control for a predator-prey system.


  1. G. J. Klir, U. S. Clair and B. Yuan, Fuzzy Set Theory: Foundation and Application, Prentice Hall, 1997.
  2. H. J. Zimmermann, Fuzzy Set Theory and its Applications, 3rd edition, Kluwer Academic, 1992.
  3. G. Bojadzieve and M. Bojadzieve, Fuzzy Sets, Fuzzy Logic Applications, World Scientific, 1995.
  4. L.H. Tsoukalas and R.E. Uhrig, Fuzzy and Neural Approaches in Engineering, John Wiley and Sons 1997.


MA508 Cryptography L-T-P-C: 3-0-0-6 Prerequisites: Nil

Cryptanalysis; Shannons Theory; Block Cipher: Data Encryption Standard, Advanced Encryption Standard, Linear and Differential Cryptanalysis; Primality Tests, Factoring Integers, Discrete Logarithm Problem; Public Key Cryptosystem: RSA; Cryptographic Hash Functions; Key Distribution and Key Agreement; Signature Schemes.


  1. DOUGLAS R. STINSON, Cryptography: Theory & Practice, Second Edition, CRC Press, 2002.
  2. ALFRED J. MENEZES, PAUL C. VAN OORSCHOT and SCOTT A. VANSTONE, Handbook of Applied Cryptography, CRC Press, 2001.



Algebraic numbers, transcendental numbers, minimal polynomial, conjugates. Number fields, primitive element, real and complex embeddings, norm, trace and discriminant. Algebraic integers, ring of integers in a number field, integral basis. Dedekind domain, ideal factorization, fractional ideal, ideal class group. Lattices, Minkowskis theory, computation of class group of number fields. Dirichlet Unit Theorem, fundamental units, units in quadratic fields, Pells equation. Cyclotomic fields.


  1. Richard A. Mollin, Algebraic Number Theory, CRC Press, 1999.
  2. J. Neukirch, Algebraic Number Theory, Springer, 1999.
  3. Stewart and Tall, Algebraic Number Theory and Fermats Last Theorem, A K Peters, 2002.


  1. Alan Baker, A Concise Introduction to Algebraic Number Theory, Cambridge University Press,1994.
  2. Janusz G.J., Algebraic Number Fields, GSM, vol-7, AMS, 1996.
  3. Marcus, D. A., Number Fields, Springer, 1977.


MA516 Semantics of Programming Languages L-T-P-C:3-0-0-6 Prerequisites: MA511 Computer Programming

Elements of a Programming language: Defining Syntax; BNF; Conditional Statements; Iterative Statements; Enumerated and Elementary Data Types; Features of Functional and Imperative languages. Elements of Mathematics: Partial and Multi Functions; Isomorphism, Duality, Zero Objects, Products, Co-Products from Category Theory; Term Algebras. Semantics: Operational, Axiomatic and Denotational Semantics of Procedural Languages; Partially Additive Semantics; Recursive Specification; Order Semantics of Recursion; Fixed- Point Semantics; Algebraic Semantics of Abstract Data Types


  1. Ellis Horowitz, Fundamentals of Programming Languages, Second Edn., Galgotia Publications, 1995.
  2. Kenneth Slonneger and Barry L. Kurtz, Formal Syntax and Semantics of Programming Languages: A Laboratory Based Approach, Addison-Wesley Publishing Company, 1995.
  3. Ernest G. Manes and Michael A. Arbib, Algebraic Approaches to Program Semantics, Texts and Monographs in Computer Science, Springer-Verlag, 1986.


  1. Carlo Ghezzi and Mehdi Jazayeri, Programming Language Concepts, Third Edn., John Wiley & Sons, 1998.
  2. Ravi Sethi, Programming Languages: Concepts and Constructs, Addison-Wesley Publishing Company, 1989.
  3. Glynn Winskel, The Formal Semantics of Programming Languages: An Introduction, The MIT Press, 1993.
  4. Hartmut Ehrig and Bernd Mahr, Fundamentals of Algebraic Specification 1: Equations and Initial Semantics, Springer-Verlag, 1985.


MA517 FUNCTIONAL AND LOGIC PROGRAMMING (3 0 0 6) Prerequisites: MA501 and MA511; or equivalent

Functional programming: functions as first class objects, laziness, data-types and pattern matching, classes and overloading, side-effects, description in languages like ML or Haskell; Lambda calculus: syntax, conversions, normal forms, Church-Rosser theorem, combinators; Implementation issues: graph reduction; Logic programming: logic and reasoning, logic programs, Prolog syntax, Horn clauses, resolution-refutation, constraint logic programming.


  1. S. Thompson, Haskell: The Craft of Functional Programming, 2nd Edition, Addison- Wesley, 1999.
  2. S. L. Peyton Jones, The Implementation of Functional Programming Languages, Prentice Hall, International Series in Computer Science, 1987.
  3. L. Stirling and E. Shapiro, The Art of Prolog: Advanced Programming Techniques, 2nd Edition, MIT Press, 1994.


  1. C. Reade, Elements of Functional Programming, Addison-Wesley, 1989.
  2. H. Barendregt, The Lambda Calculus: Its Syntax and Semantics, North Holland, 1984.
  3. J. W. Lloyd, Foundations of Logic Programming, Springer Verlag, 1987.


MA523 COMPUTER ALGEBRA L-T-P-C: 3-0- 0-6 Prerequisites: MA501 Discrete Mathematics & MA521 Modern Algebra

Algebraic numbers, Primes and factoring, Trapdoors and public key, Pseudo-random numbers. The finite Fourier transforms. The fast Fourier transform., Polynomial rings in several variables, Complexity with respect to multiplication, Shift registers and coding, Finite Boolean algebras, Equivalence classes of switching functions, Monoids and automata.


  1. L. Garding and T. Tambour, Algebra for Computer Science, Narosa, 1992
  2. W. Burnside, The Theory of Groups of Finite Order, Cambridge University Press, 1987
  3. W. Kuich and K. N. Saloma, Semirings, Automata, Languages, Springer Verlag, 1985
  4. J. J. Canon, A Language for Group Theory, (Preprint) University of Sydney, 1982.


MA544 Wavelets and Applications L-T-P-C:3-0-0-6 Prerequisites: MA543 Functional Analysis

Reviews of Fourier analysis and LP spaces. Wavelets and atomic decomposition of functions, Multiresolution signal decomposition, Multiresolution analysis and the construction of wavelets, Examples of wavelets,QMF and fast wavelet transform, Localization, Regularity and approximation properties of wavelets. Construction of compactly support wavelets, Orthonormal bases of compactly supported wavelets, Wavelets sampling techniques, Convergence of Wavelet expansion, Time-frequency analysis for signal processing, Applications of wavelets in image and signal processing.



  1. Y. Meyer, Wavelets: Algorithm and Application , SIAM, 1993.
  2. E. Aboufadel and S. Schlicker, Discovering Wavelets , John Wiley and Sons, 1999.
  3. G. Kaiser, A Friendly Guide to Wavelets, Birkhauser, 1994.


MA 545 Complex Dynamics and Fractals L-T-P-C:3-0-0-6 Prerequisites: Nil

Classical Fractals, Cantor set, Sierpinski triangle, Von Koch curve, Hilbert and Peano curves, Weierstrass function. Self-similarity, Scaling, Similarity dimension, Box-counting dimension, Information dimension, Capacity dimension. Foundations of iterated function systems (IFS), Classical fractals generated by IFS, Contractions mapping principle, Collage theorems, Fractal image compression, Image encoding and decoding by IFS. Iteration of quadratic polynomials, Julia sets, Fatou sets, Mandelbrot set, Characterization of Julia sets, Dynamics of functions ez , sin z and cos z, Bifurcation and chaotic burst.


Texts / References:

  1. M. F. Barnsley, Fractals Everywhere , 2nd edition, Academic Press, 1995.
  2. Ning Lu, Fractal Imaging, Academic Press, 1997.
  3. M. J. Turner et. al, Fractal Geometry in Digital Imaging, Academic Press, 1998.
  4. A. F. Beardon, Iteration of Rational Functions, Springer Verlag, 1991.
  5. L. Carleson and T.W. Gamelin, Complex Dynamics, Springer Verlag, 1993.


MA 546 Differential Algebraic Equations L-T-P-C:3-0-0-6 Prerequisites: Nil

Classification of DAEs, Solvability and Index, Linear constant coefficient DAEs, Linear time varying DAEs, Nonlinear systems, Index reduction and constraint stabilization. Runge-Kutta method for DAEs, Classes of implicit Runge-Kutta methods, Convergence analysis for Index 1, Index 2, and Index 3 systems. Solution of nonlinear systems by Newtons method, Local error estimation. Multistep methods, BDF convergence, Semi explicit index 1 systems, Fully implicit index 1 systems, Semi explicit index 2 systems, Index 3 systems of Hessenberg form, BDF methods, Stiff Problems and applications.

Software Support: DASSL, RADAU5

Texts / References:

  1. E. Hairer, C. Lubich and M. Roche, The Numerical Solution of Differential Algebraic Systems by Runge-Kutta Methods, Springer Verlag, 1989.
  2. K.E. Brenan, S. L. Campbell and L. R. Petzold, Numerical Solution of Initial Value Problems in Differential Algebraic Equations , North Holland, 1989.


MA548 Fourier Theory 3-0-0-6 Prerequisites: Nil

Convolutions ? Fourier Transform ? Two Domains - Fourier Transform properties: scaling, shifting, convolution, correlation theorems ? Parseval?s theorem ? Sampling Theorem ? Discrete Fourier Transform ? Fast Fourier Transform - Tempered Distributions ? Fourier Transform on Tempered Distributions.

Texts / References:

  1. Ronald N. Bracewell, The Fourier Transform and its Applications, Mc Graw Hill, New York, 1986
  2. C. S. Burns and T. W. Parks, DFT/FFT and Convolution Algorithms: Theory and Implementation, John Wiley and Sons, New York, 1985.
  3. Jack D. Gaskill, Linear Systems, Fourier Transforms and Optics, John Wiley and Sons, New York, 1978.
  4. W. Rudin, Functional Analysis, Tata McGraw Hill, 1974.


MA549 Topology 3-0-0-6 Prerequisites: Nil

Topological spaces, Basis for a topology, Limit points and closure of a set, Continuous and open maps, Homeomorphisms, Subspace topology, Product and quotient topology. Connected and locally connected spaces, Path connectedness, Components and path components, Compact and locally compact spaces, One point compactification. Countability axioms, Separation axioms, Urysohn?s Lemma, Urysohn?s metrization theorem, Tietze extension theorem, Tychonoff?s theorem, Completely Regular Spaces, Stone-Cech Compactification.


  1. G.F. Simmons, Introduction to Topology and Modern Analysis, McGraw Hill, 1963.
  2. James R. Munkres, Topology, Second Edition, Prentice Hall, 1999.


  1. Stephan Willard, General Topology, Dover, 2004.
  2. Klaus Janich, Topology, Springer, UTM, 1984.
  3. James Dugundji, Topology, McGraw Hill, 1966.


MA 550 MEASURE THEORY (3 0 0 6) Prerequisites: Nil

Algebras and ólgebras, measures, outer measures, measurable sets, Lebesgue measure and its properties, non-measurable sets, measurable functions and their properties, Egoroff?s theorem, Lusin?s theorem; Lebesgue Integration: simple functions, integral of bounded functions over a set of finite measure, bounded convergence theorem, integral of nonnegative functions, Fatou?s lemma, monotone convergence theorem, the general Lebesgue integral, Lebesgue convergence theorem, change of variable formula; Differentiation and integration: functions of bounded variation, differentiation of an integral, absolute continuity; Signed and complex measures, Radon-Nikodym theorem, Lp -spaces and their dual; Product measures, constructions, Fubini?s theorem and its applications.


  1. D. L. Cohn, Measure Theory, 1st Edition, Birkhauser, 1994.
  2. G. de Barra, Measure Theory and Integration, New Age International, 1981.


  1. M. Capinski and E. Kopp, Measure, Integral and Probability, 2nd Edition, Springer, 2007.
  2. H. L. Royden, Real Analysis, 3rd Edition, Prentice Hall/Pearson Education, 1988.
  3. I. K. Rana, An Introduction to Measure and Integration, Narosa, 1997.


MA561 Fluid Dynamics [3-0-0-6] Prerequisites: Nil

Review of gradient, divergence and curl. Elementary idea of tensors. Velocity of fluid, Streamlines and path lines, Steady and unsteady flows, Velocity potential, Vorticity vector, Conservation of mass, Equation of continuity. Equations of motion of a fluid, Pressure at a point in fluid at rest, Pressure at a point in a moving fluid, Eulers equation of motion, Bernoullis equation. Singularities of flow, Source, Sink, Doublets, Rectilinear vortices. Complex variable method for two-dimensional problems, Complex potentials for various singularities, Circle theorem, Blasius theorem, Theory of images and its applications to various singularities. Three dimensional flow, Irrotational motion, Weisss theorem and its applications. Viscous flow, Vorticity dynamics, Vorticity equation, Reynolds number, Stress and strain analysis, Navier-Stokes equation, Boundary layer Equations


  1. N. Curle and H. Davies, Modern Fluid Dynamics, Van Nostrand Reinhold, 1966.
  2. L. M. Milne Thomson, Theoretical Hydrodynamics, Macmillan and Co., 1960.
  3. G. K. Batchelor, An Introduction to Fluid Dynamics, Cambridge University Press, 1993.
  4. F. Chorlton, A Text Book of Fluid Dynamics, Von Nostrand Reinhold/CBS, 1985.
  5. A. R. Patterson, A First Course in Fluid Dynamics, Cambridge University Press, 1992.


MA562 Mathematical Modelling and Numerical Simulation [3-0-0-6] Prerequisites: Nil

Model and its different types, Finite models, Statistical models, Stochastic models, Formulation of a model, Laws and conservation principles, Discrete and continuous models, Manipulation into its most respective form, Evaluation of a model. Case studies, Continuum model, Transport phenomena, Diffusion and air pollution models, Microwave heating, Communication and Information technology.



  1. R. Aris, Mathematical Modelling Techniques, Dover, 1994.
  2. C. L. Dym and E. S. Ivey, Principles of Mathematical Modelling, Academic Press, 1980.
  3. M. S. Klamkin, Mathematical Modelling: Classroom Notes in Applied Mathematics, SIAM, 1986.
  4. A. Friedman and W. Littman, Industrial Mathematics for Undergraduates, SIAM, 1994.
  5. Y. C. Fung, A First Course in Continuum Mechanics, Prentice Hall, 1969.
  6. E. N. Lightfoot, Transport Phenomenon and Living Systems, Wiley, 1974.
  7. M. Braun, C. S. Coleman and D. A. Drew, Differential Equation Models, Modules in Applied Mathematics, Volume1, Springer Verlag, 1978.


MA 563 Calculus of Variations and Optimal Control L-T-P-C:3-0-0-6 Prerequisites: MA542 Differential Equations

The concept of variation and its properties, Variational problems with fixed boundaries, The Euler equation, Variational problems in parametric form. Variational problems with moving boundaries, Reflection and refraction extremals. Sufficient conditions for an extremum, Canonical equations and variational principles, Complementary variational principles, The Hamilton-Jacobi equation. Direct methods for variational problems, Rayleigh-Ritz method, Galerkin method. Introduction to optimal control problems, Controllability and optimal control, Isoperimetric problems, Bolza problem, Optimal time of transit, Rocket propulsion problem, Linear autonomous time-optimal control problem, Existence theorems for optimal control problems, Necessary conditions for Optimal controls, The Pontryagin maximum principle.

Texts / References:

  1. A. S. Gupta, Calculus of Variation with Applications, Prentice-Hall, India, 1997.
  2. J. Macki and A. Strauss, Introduction to Optimal Control Theory, UTM, Springer, 1982.
  3. G. M. Ewing, Calculus of Variations with Applications, Dover, 1985.
  4. H. Sagan, Introduction to Calculus of Variations, Dover, 1967.
  5. J. L. Troutman, Variational Calculus and Optimal Control, 2nd edition, Springer Verlag, 1996.


MA575 Mathematical Methods [3-0-0-6] Prerequisites: Nil

General solution of Bessel equation, Recurrence relations, Orthogonal sets of Bessel functions, Modified Bessel functions, Applications. General solution of Legendre equation, Legendre polynomials, Associated Legendre polynomials, Rodrigues formula, Orthogonality of Legendre polynomials, Application. Concept and calculation of Greens function, Approximate Greens function, Greens function method for differential equations, Fourier Series, Generalized Fourier series, Fourier Cosine series, Fourier Sine series, Fourier integrals. Fourier transform, Laplace transform, Z-transform, Hankel transform, Mellin transform. Solution of differential equation by Laplace and Fourier transform methods.


  1. G. N. Watson, A Treatise on the Theory of Bessel Functions, Cambridge University Press, 1944.
  2. G. F. Roach, Greens Functions, Cambridge University Press, 1995.
  3. A. D. Poularikas, The Transforms and Applications Handbook, CRC Press, 1996.
  4. J. W. Brown and R. Churchill, Fourier Series and Boundary Value Problems, McGraw Hill, 1993.


MA576 Large Scale Scientific Computation [3-0-0-6] Prerequisites: MA571 Scientific Computing, MA574 Numerical Linear Algebra

Large sparse linear systems, Storage schemes, Review of stationary iterative process, Krylov subspace methods, Conjugate gradients(CG), BiCG, MINRES and GMRES, The Lanczos iteration, From Lanczos to Gauss quadrature, Preconditioning, Error bounds for CG and GMRES, Effects of finite precision arithmetic, Multigrid methods, Multigrid as a preconditioner for Krylov subspace methods. Nonlinear systems, Newtons method and some of its variants, Newton GMRES, Continuation methods, Conjugate direction method, Davidon-Fletcher-Powell Algorithms.

Software Support: HOMPACK, LAPACK.

System Support: PARAM.


  1. J. M. Ortega and W. C. Rheinboldt, Iterative Solution of Nonlinear Equations in Several Variables, Academic Press, 1970.
  2. C. T. Kelly, Iterative Methods for Linear and Nonlinear Equations, SIAM, Philadelphia, 1995.
  3. A. Greenbaum, Iterative Methods for Solving Linear Systems, SIAM, Philadelphia, 1997.
  4. O. Axelsson, Iterative Solution Methods, Cambridge University Press, 1994.
  5. P. Wesseling, An Introduction to Multigrid Methods, John Wiley & Sons, 1992.
  6. C. W. Ueberrhuber, Numerical Computation: Methods, Software and Analysis, Springer Verlag, 1997.
  7. I. S. Duff, A.M.Erisman and J. K. Reid, Direct Methods for Sparse Matrices, Oxford University Press, 1986.


MA577 Perturbation Methods [3-0-0-6]

Asymptotic expansion and approximations, Asymptotic solution of algebraic and transcendental equations, Introduction to the asymptotic solution of differential equations. Regular Perturbations, Perturbed second order differential equations, Dimensional analysis, Initial and boundary value problems, Partial differential equations, Error estimation. Multiple scales, Overview of multiple scales and averaging, The first order two-scale approximation, Higher order approximations. Methods of WKB type, Introductory examples, The formal WKB expansion without turning points, Ray methods


  1. 1. J. A. Murdock, Perturbations Theory and Methods, John Wiley and Sons, 1991.
  2. 2. M. H. Holmes, Introduction to Perturbation Methods, Springer Verlag, 1995.


MA578 Computational Fluid Dynamics [3-0-0-6] Prerequisites: MA561 Fluid Dynamics

Incompressible plane flows, Stream function and vorticity equations, Conservative form and normalizing systems, Method for solving vorticity transport equation, Basic finite difference forms, Conservative property, Convergence and stability analysis, Explicit and implicit methods, Stream function equation and boundary conditions, Schemes for advective diffusion equation, Upwind differencing and artificial vorticity, Solution for primitive variables.

Software Support: CFD Software Packages.


  1. C. A. J. Fletcher, Computational Techniques for Fluid Dynamics, Volume 1 & 2, Springer Verlag, 1992.
  2. C. Y. Chow, Introduction to Computational Fluid Dynamics, John Wiley, 1979.
  3. M. Holt, Numerical Methods in Fluid Mechanics, Springer Verlag, 1977.
  4. H. J. Wirz and J. J. Smolderen, Numerical Methods in Fluid Dynamics, Hemisphere, 1978.
  5. D. A. Anderson, J. C. Tannehill and R. H. Pletcher, Computational Fluid Dynamics and Heat Transfer, McGraw Hill, 1984.


MA 593 Statistical Methods and Time Series Analysis L-T-P-C:3-0-0-6 Prerequisites: MA592 Probability, Random Processes and Statistical Inference

Review of sampling distributions. Point and interval-estimation, Hypothesis testing, Likelihood ratio procedure, Bayesian methods. Introduction to decision theory. Regression methods, Linear, Multilinear and polynomial regression. Model checking. Time series analysis, Introduction to autocorrelation function, linear stationary models like autoregressive, integrated moving average processes, Yule-Walker equations and partial auto correlations, Forecasting.

Software Support: Statistical packages like SAS and SPSS.

Texts / References:

  1. A. B. Bowker and G. J. Libermann, Engineering Statistics, Asia, 1972.
  2. N. L. Johnson and F. C. Xeen Leone, Statistics and Experimental Design in Engineering and the Physical Sciences, Volumes 1 and 2, 2nd edition, Wiley, 1977.
  3. C. Chatfield, The Analysis of Time Series: An Introduction, Chapman and Hall, 1984.
  4. G. E. P. Box and G. M. Jenkins, Time Series Analysis Forecasting and Control, Holden-Day, 1976.


MA594 Statistical Decision Theory [3-0-0-6] Prerequisites: MA592 Probability and Statistical Inference

Decision functions, Risk functions, Utility and subjective probability, Randomization, Optimal decision rules. Admissibility and completeness, Existence of Bayes decision rules, Existence of a minimal complete class, Essential completeness of the class of nonrandomized rules. The minimax theorem. Invariant statistical decision problems. Multiple decision problems. Sequential Decision problems.


  1. J. O. Berger, Statistical Decision Theory: Foundations, Concepts and Methods, Springer Verlag, 1980.
  2. T. S. Ferguson, Mathematical Statistics, Academic Press, 1967.


MA 595 Stochastic Programming and Applications L-T-P-C:3-0-0-6 Prerequisites: MA592 Probability, Random Processes and Statistical Inference

Quadratic and nonlinear programming solution methods applied to chance constrained programming problems. Stochastic linear and nonlinear programming problems arising in inventory control and other industrial applications. Queuing models of computer networks, Information processing under uncertainty, Two stage and multi stage solution techniques. Use of Monte carlo, Probabilistic and heuristic algorithms, Genetic algorithms and neural networks for adaptive optimization.

Texts / References:

  1. J. K. Sengupta, Stochastic Optimizations and Economic Models, Dordrecht Reidel, 1986.
  2. Yu. Ermoliev and R. J. B. Wets, Numerical Techniques for Stochastic Optimization, Springer Verlag, 1988.
  3. K. Schittkowski, More Test Examples of Nonlinear Programming Codes , Springer Verlag, 1987.
  4. Z. Michaeleawicz, General Algorithms + Data Structures =Evolution Program, Springer Verlag, 1992


MA 596 STOCHASTIC CALCULUS FOR FINANCE L-T-P-C:3-0-0-6 Prerequisites: Nil

Basic ideas of hedging and pricing by arbitrage. Basic concepts from probability theory and stochastic processes, conditional expectation, martingales, random walk, Markov processes, Brownian motion. Stochastic integration, Itôintegral, Itôformula. Stochastic differential equations. Risk-neutral pricing, Black-Scholes-Merton option pricing model, Girsanov?s Theorem, American derivative securities, term-structure models. Jump processes and their application to option pricing.


  1. S.E. Shreve, Stochastic Calculus for Finance I: The Binomial Asset Pricing Model, Springer, 2005.
  2. S.E. Shreve, Stochastic Calculus for Finance II: Continuous-Time Models, Springer, 2005.
  3. J.M. Steele, Stochastic Calculus and Financial Applications, Springer, 2001.


  1. T. Mikosh, Elementary Stochastic Calculus, with Finance in View, World Scientific, 1998.
  2. F.C. Klebaner, Introduction to Stochastic Calculus with Applications, World Scientific, 2005.


MA 597 QUEUEING THEORY AND APPLICATIONS (3 0 0 6) Prerequisites: Nil

Review of probability, random variables, distributions, generating functions; Poisson, Markov, renewal and semi-Markov processes; Characteristics of queueing systems, Little?s law, Markovian and non-Markovian queueing systems, embedded Markov chain applications to M/G/1, G/M/1 and related queueing systems; Networks of queues, open and closed queueing networks; Queues with vacations, priority queues, queues with modulated arrival process, discrete time queues, introduction to matrix-geometric methods, applications in manufacturing, computer and communication networks.


  1. D. Gross and C. Harris, Introduction to Queueing Theory, 3rd Edition, Wiley, 1998 (WSE Edition, 2004)
  2. L. Kleinrock, Queueing Systems, Vol. 1: Theory, John Wiley, 1975.
  3. J. Medhi, Stochastic Models in Queueing Theory, 2nd Edition, Academic Press, 2003 (Elsevier India Edition, 2006).


  1. J.A. Buzacott and J.G. Shanthikumar, Stochastic Models of Manufacturing Systems, Prentice Hall, 1992.
  2. R. B. Cooper, Introduction to Queueing Theory, 2nd Edition, North-Holland, 1981.
  3. L. Kleinrock, Queueing Systems, Vol. 2: Computer Applications, John Wiley, 1976.
  4. R. Nelson, Probability, Stochastic Processes, and Queueing Theory: The Mathematics of Computer Performance Modelling, Springer, 1995.
  5. E. Gelenbe and G. Pujolle, Introduction to Queueing Networks, 2nd Edition, Wiley, 1998.



Probability, random variables, probability distributions, expectations, martingales, Brownian motion, Itôtegral, Itôformula; Financial markets and financial instruments, forward and futures contracts and determination of their prices, options, mechanism of options markets, put-call parity, European and American options, risk-neutral valuation, Cox-Ross-Rubinstein model, Black-Scholes-Merton model; Numerical methods for European and American options.


  1. P. Wilmott, S. Howison and J. Dewynne, The Mathematics of Financial Derivatives, Cambridge University Press, 1995.
  2. S. Roman, Introduction to the Mathematics of Finance: From Risk Management to Options Pricing, Springer, 2004.
  3. J. C. Hull, Options, Futures and Other Derivatives, 6th Edition, Prentice Hall of India/Pearson Education, 2006.


  1. M. Capinski and T. Zastawniak, Mathematics for Finance: An Introduction to Financial Engineering, Springer, 2005.
  2. D. Higham, An Introduction to Financial Option Valuation: Mathematics, Stochastics and Computation, Cambridge University Press, 2004.


MA 611 DIFFERENTIAL GEOMETRY OF CURVES AND SURFACES (3-0-0-6) Prerequisites: MA541 or equivalent

Local theory of plane and space curves, Curvature and torsion formulas, Serret-Frenet formulas, Fundamental Theorem of space curves. Regular surfaces, Change of parameters, Differentiable functions, Tangent plane, Differential of a map. First and second fundamental form. Orientation, Gauss map and its properties, Euler?s Theorem on principal curvatures. Isometries, and Gauss? Theorema Egregium. Parallel transport, Geodesics, Gauss-Bonnet theorem and its applications to surfaces of constant curvature. Hopf-Rinow?s theorem, Bonnet?s theorem, Jacobi fields, Theorems of Hadamard. Riemanns Habilitationsvortrag.


  1. Manfredo P. Do Carmo, Differential Geometry of Curves and Surfaces, Prentice Hall, 1976.
  2. John McCleary, Geometry from a Differentiable Viewpoint, Cambridge University Press, 1994.
  3. Michael Spivak, A Comprehensive Introduction to Differential Geometry, Publish or Perish, 1994.
  4. Barret O?Neill, Elementary Differential Geometry, Academic Press, Second Edition, 1997
  5. Carl Friedrich Gauss, General Investigations of Curved Surfaces, Edited with an Introduction and Notes by Peter Pesic, Dover 2005.
  6. Andrew Pressley, Elementary Differential Geometry, Springer 2002.


MA 621 Rings and Modules (3-0-0-6) Prerequisites: MA 521 (Modern Algebra) or equivalent

Brief review of rings and ideals, nilradical and Jacobson radicals, extension and contraction; basic theory of modules: submodules and quotient modules, module homomorphisms, annihilators, torsion submodules, irreducible modules, Schurs lemma, direct sum and product of modules, free modules, localization, Nakayamas lemma; Exact sequences, short and split exact sequences, projective modules, injective modules, Baers criterion for injective modules; tensor product of modules, universal property of tensor product, exactness property of tensor products, flat modules; chain conditions on rings, Noetherian rings, Hilbert basis theorem; Artinian rings, discrete valuation rings, Dedekind domains, fractional ideals, ideal class groups.


  1. M.F. Atiyah and I.G. MacDonald, Introduction to Commutative Algebra, Addison Wesley, 1969.
  2. D.S. Dummit and R.M. Foote, Abstract Algebra, John Wiley & Sons, Inc., II Edition, 1999.
  3. S. Lang, Algebra, III edition, Springer, 2004.
  4. P A. Grillet, Abstract Algebra, II edition, Springer 2006
  5. T .W. Hungerford, Algebra, Springer, 1996


MA 622 Galois Theory (3-0-0-6) Prerequisites: MA521 (Modern Algebra) or equivalent

Field extensions, algebraic extensions, minimal polynomials, separable and normal extensions; Automorphism groups, fixed fields, Galois extensions, Galois groups; Fundamental theorem of Galois theory, Galois closure; Galois groups of finite fields; Cyclotomic extensions, abelian extensions over rationals, Kronecker-Weber theorem; Galois groups of polynomials, symmetric functions, discriminant, Galois groups of quadratic, cubic and quartic polynomials; solvable extensions, radical extensions, solution of polynomial equations in radicals, insolvability of the quintic; cyclic extensions, Kummer theory, Artin-Schreier extensions; Galois groups over rationals, transcendental extensions, infinite Galois groups.

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  1. D.S. Dummit and R.M. Foote, Abstract Algebra, John Wiley & Sons, Inc., II Edition, 1999.
  2. I. Stewart, Galois Theory, Academic Press, 1989.
  3. J.P. Escofier, Galois Theory, Springer, 2001.
  4. Emil Artin, Galois Theory, University of Notre Dame Press,1971


MA 623 Introduction to Algebraic Geometry (3-0-0-6) Prerequisites: MA521 (Modern Algebra) or equivalent

Filtrations, graded rings, graded modules; Krull dimension, depth, going up and going down, primary ideals, prime avoidance theorem, primary decomposition; Hilbert functions, regular local rings, Cohen-Macaulay rings; Zariski topology, Hilbert Nullstellensatz, spectrum of a ring, affine algebraic sets, affine variety, projective variety; Noether normalization, integral closure; algebraic curves, function field of a curve, divisors, principal divisors, Picard group, divisor class group, genus; monomial orderings, division algorithm, Groebner basis, syzygies.


  1. M. F. Atiyah & I. Macdonald, Commutative Algebra, Addison-Wesley, 1969
  2. I. Shafarevich, Basic Algebraic Geometry, Springer, 1972
  3. D. Eisenbud, Commutative Algebra, Springer, 1995
  4. 4.R. Hartshone, Algebraic Geometry, Springer, 1997
  5. J. Harris, Algebraic Geometry: A First Course, Springer, 1995
  6. E. Kunz, Introduction To Commutative Algebra And Algebraic Geometry, Birkhauser, 1984


MA 651 Distributed Computing (3-0-0-6) Prerequisites: CS341 or equivalent

Overview of distributed computing, Basic algorithms in message-passing systems, Coordination algorithms, Causality and logical time, Distributed snapshot and global states, Distributed algorithms for graphs, fault and fault-tolerance, Distributed mutual exclusion, distributed transactions, distributed consensus and agreement algorithms, Self-stabilization, Applications in wireless sensor networks.


  1. H. Attiya and J. Welch, Distributed Computing, McGraw-Hill, 1998.
  2. N. Lynch, Distributed Algorithms, Morgan Kaufmann, 1996.
  3. A. Tannenbaum, M. van Steen, Distributed Systems: Principles and Paradigms, Prentice Hall, 2002.
  4. S. Ghosh, Distributed Systems: An Algorithmic Approach, CRC Press, 2006.
  5. A. Kshemkalyani, M. Singhal, Distributed Computing: Principles, Algorithms, and Systems, Cambridge University
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