RGPV TET Mathematics Syllabus | Download RGPV TET Engg mATHS Syllabus
RGPV TET Mathematics Syllabus | Download RGPV TET Engg mATHS Syllabus
Linear Algebra: Finite dimensional vector spaces; Linear transformations and their matrix
representations, rank; systems of linear equations, eigen values and eigen vectors, minimal
polynomial, Cayley-Hamilton Theroem, diagonalisation, Hermitian, Skew-Hermitian and unitary
matrices; Finite dimensional inner product spaces, Gram-Schmidt orthonormalization process,
self-adjoint operators.
Complex Analysis: Analytic functions, conformal mappings, bilinear transformations; complex
integration: Cauchy’s integral theorem and formula; Liouville’s theorem, maximum modulus
principle; Taylor and Laurent’s series; residue theorem and applications for evaluating real
integrals.
Real Analysis: Sequences and series of functions, uniform convergence, power series, Fourier
series, functions of several variables, maxima, minima; Riemann integration, multiple integrals,
line, surface and volume integrals, theorems of Green, Stokes and Gauss; metric spaces,
completeness, Weierstrass approximation theorem, compactness; Lebesgue measure, measurable
functions; Lebesgue integral, Fatou’s lemma, dominated convergence theorem.
Ordinary Differential Equations: First order ordinary differential equations, existence and
uniqueness theorems, systems of linear first order ordinary differential equations, linear ordinary
differential equations of higher order with constant coefficients; linear second order ordinary
differential equations with variable coefficients; method of Laplace transforms for solving
ordinary differential equations, series solutions; Legendre and Bessel functions and their
orthogonality.
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Algebra:Normal subgroups and homomorphism theorems, automorphisms; Group actions,
Sylow’s theorems and their applications; Euclidean domains, Principle ideal domains and unique
factorization domains. Prime ideals and maximal ideals in commutative rings; Fields, finite
fields.
Functional Analysis:Banach spaces, Hahn-Banach extension theorem, open mapping and closed
graph theorems, principle of uniform boundedness; Hilbert spaces, orthonormal bases, Riesz
representation theorem, bounded linear operators.
Numerical Analysis: Numerical solution of algebraic and transcendental equations: bisection,
secant method, Newton-Raphson method, fixed point iteration; interpolation: error of polynomial
interpolation, Lagrange, Newton interpolations; numerical differentiation; numerical integration:
Trapezoidal and Simpson rules, Gauss Legendrequadrature, method of undetermined parameters;
least square polynomial approximation; numerical solution of systems of linear equations: direct
methods (Gauss elimination, LU decomposition); iterative methods (Jacobi and Gauss-Seidel);
matrix eigenvalue problems: power method, numerical solution of ordinary differential
equations: initial value problems: Taylor series methods, Euler’s method, Runge-Kutta methods.
Partial Differential Equations: Linear and quasilinear first order partial differential equations,
method of characteristics; second order linear equations in two variables and their classification;
Cauchy, Dirichlet and Neumann problems; solutions of Laplace, wave and diffusion equations in
two variables; Fourier series and Fourier transform and Laplace transform methods of solutions
for the above equations.
Mechanics: Virtual work, Lagrange’s equations for holonomic systems, Hamiltonian equations.
Topology: Basic concepts of topology, product topology, connectedness, compactness,
countability and separation axioms, Urysohn’s Lemma.
Probability and Statistics: Probability space, conditional probability, Bayes theorem,
independence, Random variables, joint and conditional distributions, standard probability
distributions and their properties, expectation, conditional expectation, moments; Weak and
strong law of large numbers, central limit theorem; Sampling distributions, UMVU estimators,
maximum likelihood estimators, Testing of hypotheses, standard parametric tests based on
normal, X
2
, t, F – distributions; Linear regression; Interval estimation.
Linear programming: Linear programming problem and its formulation, convex sets and their
properties, graphical method, basic feasible solution, simplex method, big-M and two phase
methods; infeasible and unbounded LPP’s, alternate optima; Dual problem and duality theorems,
dual simplex method and its application in post optimality analysis; Balanced and unbalanced
transportation problems, u -u method for solving transportation problems; Hungarian method for
solving assignment problems.
Calculus of Variation and Integral Equations: Variation problems with fixed boundaries;
sufficient conditions for extremum, linear integral equations of Fredholm and Volterra type, their
iterative solutions.
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