I SY E 726: Nonlinear Optimization I
- Catalog Description:
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.
- Credits: 3
- Prerequisites: Familiarity with basic mathematical analysis and either Math 443 or 320; or cons inst
- Official Course Description (pdf)
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