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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.

Prerequisites

Graduate/professional standing

Satisfies
Credits

Not Reported

Offered

Not Reported

Grade Point Average
3.31

1.12% from Historical

Completion Rate
100%

2.5% from Historical

A Rate
14.29%

-56.36% from Historical

Class Size
21

-40.69% from Historical

Cumulative Grade Distribution

Instructors (2026 Summr)

Sorted by ratings from Rate My Professors

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