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Jan 15, 2025
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MATH 216 - Nonlinear Optimization
Semester Hours: 3
Nonlinear optimization problems are classified, and algorithms are surveyed. These include Newton’s method, gradient method, and variants for unconstrained optimization, Lagrange multiplier method, feasible point methods, and penalty/barrier methods for constrained optimization. Convexity of functions and domains, linear and nonlinear constraints, is discussed to ascertain the algorithms’ applicability. Applications will be presented.
Prerequisite(s)/Course Notes:
MATH 215 or MATH 228 . Course open to graduate students in Computer Science/Data Science, others need permission from computer science graduate director.
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