Nonlinear Optimization I

CS 726



Theory and algorithms for nonlinear optimization, focusing on unconstrained optimization. Line-search and trust-region methods; quasi-Newton methods; conjugate-gradient and limited-memory methods for large-scale problems; derivative-free optimization; algorithms for least-squares problems and nonlinear equations; gradient projection algorithms for bound-constrained problems; and simple penalty methods for nonlinearly constrained optimization. Prereq: Familiarity with basic mathematical analysis (e.g., Math 521) and either Math. 443 or 320, or consent of instructor.

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