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  #2  
11th January 2016, 03:38 PM
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Re: IIT Mumbai Applied Mathematics

I am a student of the IIT Mumbai so I need some books for the Applied Mathematics so can you sugget me some books
  #3  
11th January 2016, 03:39 PM
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Join Date: Apr 2013
Re: IIT Mumbai Applied Mathematics

Hi friend as you want some good books for the IIT Mumbai Applied Mathematics so I am giving you the same with course

SI 507 Numerical Analysis 3 0 2 8


J. Stoer and R. Bulirsch, Introduction to Numerical Analysis, 2nd ed., Texts in Applied Mathematics, Vol. 12, Springer Verlag, New York, 1993.


SI 512 Finite Difference Methods for Partial Differential Equations

Partial Differential Equations of Applied Mathematics, 2nd ed., Wiley, 1989.

MA 417 Ordinary Differential
Equations 3 1 0 8


L. Perko, Differential Equations and Dynamical Systems, Texts in Applied Mathematics, Vol. 7, 2nd ed., Springer Verlag, New York, 1998.




MA 508 Mathematical Methods 3 1 0 6

J. Kevorkian and J.D. Cole, Perturbation Methods in Applied Mathematics, Springer Verlag, Berlin, 1985.


MA 515 Partial Differential
Equations 3 1 0 8

E. Zauderer, Partial Differential Equations of Applied Mathematics, 2nd ed., John Wiley and Sons, New York, 1989.

MA 540 Numerical Methods for Partial
Differential Equations 2 1 0 6


J.W. Thomas, Numerical Partial Differential Equations : Finite Difference Methods, Texts in Applied Mathematics, Vol. 22, Springer Verlag, NY, 1999.

J.W. Thomas, Numerical Partial Differential Equations: Conservation Laws and Elliptic Equations, Texts in Applied Mathematics, Vol. 33, Springer Verlag, NY, 1999.

I am also giving you the syllabus for one of the above subject


SI 507 Numerical Analysis 3 0 2 8

Principles of floating point computations and rounding errors.

Systems of Linear Equations: factorization methods, pivoting and scaling, residual error correction method.

Iterative methods: Jacobi, Gauss-Seidel methods with convergence analysis, conjugate gradient methods.

Eigenvalue problems: only implementation issues.

Nonlinear systems: Newton and Newton like methods and unconstrained optimization.

Interpolation: review of Lagrange interpolation techniques, piecewise linear and cubic splines, error estimates.

Approximation : uniform approximation by polynomials, data fitting and least squares approximation.

Numerical Integration: integration by interpolation, adaptive quadratures and Gauss methods

Initial Value Problems for Ordinary Differential Equations: Runge-Kutta methods, multi-step methods, predictor and corrector scheme, stability and convergence analysis.

Two Point Boundary Value Problems : finite difference methods with convergence results.
Lab. Component: Implementation of algorithms and exposure to public domain packages like LINPACK and ODEPACK.

Texts / References

K.E. Atkinson, An Introduction to Numerical Analysis, Wiley, 1989.

S.D. Conte and C. De Boor, Elementary Numerical Analysis %G–%@ An Algorithmic Approach, McGraw-Hill, 1981.

K. Eriksson, D. Estep, P. Hansbo and C. Johnson, Computational Differential Equations, Cambridge Univ. Press, Cambridge, 1996.


G.H. Golub and J.M. Ortega, Scientific Computing and Differential Equations: An Introduction to Numerical Methods, Academic Press, 1992.

J. Stoer and R. Bulirsch, Introduction to Numerical Analysis, 2nd ed., Texts in Applied Mathematics, Vol. 12, Springer Verlag, New York, 1993.


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