Syllabus

Vector spaces and matrices, transformations, eigenvalues and eigenvectors, norms; geometrical concepts ‐ hyperplanes, convex sets, polytopes and polyhedra; unconstrained optimization ‐ condition for local minima; one dimensional search methods ‐ golden section, fibonacci, newtons, secant search methods; gradient methods ‐ steepest descent; newton's method, conjugate direction methods, conjugate gradient method; constrained optimization ‐ equality conditions, lagrange condition, second order conditions; inequality constraints ‐ karush‐kuhntucker condition; convex optimization; introduction to assignment problem, decision analysis dynamic programming and linear programming.