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NONLINEAR OPTIMIZATION I

COMPSCI/ISYE/MATH/STAT 726
Course 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. Students are strongly encouraged to have knowledge of linear algebra and familiarity with basic mathematical analysis.

Prerequisties

Graduate/professional standing

Satisfies
Credits

3

Offered

Occasionally

Grade Point Average
3.07

-6.31% from Historical

Completion Rate
95.65%

-1.89% from Historical

A Rate
26.09%

-21.53% from Historical

Class Size
23

-36.28% from Historical

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

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