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  • 7:09 AM, Wednesday, 22 Jan 2020


Course Postgraduate
Semester Electives
Subject Code MA872
Subject Title Advanced Optimization

Syllabus

Unconstrained Optimization: line search method: Wolf condition, Goldstein condition, sufficient
decrease and backtracking, Newtons method and Quazi Newton method; trust region method:
the Cauchy point, algorithm based on Cauchy point, improving on the Cauchy point, the Dog-
leg method, two-dimensional subspace reduction; nonlinear conjugate gradient method: the
Fletcher Reeves method.
 
Constrained Optimization: penalty method, quadratic penalty method, convergence, non smooth
penalty function, L1 penalty method, augmented Lagrangian method; quadratic programming,
Schur complementary, null space method, active set method for convex QP; sequential quadratic
programming, convex programming.

Text Books

Same as Reference

References

1. Boyd, S. and Vandenberghe, L., Convex Optimization, Cambridge Univ. Press (2004).
2. Nocedel, J. and Wright, S. Numerical Optimization, Springer (2006).