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Apr 16, 2024
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MA 344 - Nonlinear Programming3 lecture hours 0 lab hours 3 credits
Course Description
A course on the fundamentals of nonlinear optimization. Topics include convex sets and functions, necessary and sufficient optimality conditions, duality in convex optimization, and algorithms for unconstrained and constrained optimization problems. Also includes a brief introduction to semidefinite programming. (prereq: MA 231 , MA 343 )
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
- Understand the difference between linear, integer, nonlinear and semidefinite programs, as well as the levels their computational complexities
- Learn the basic properties of convex sets and functions
- Solve small constrained and unconstrained convex nonlinear programs by hand
- Understand and be able to verify the Karush-Kuhn-Tucker optimality conditions
- Understand the notion of duality in convex optimization
Prerequisites by Topic
- The basic principles of algebra
- Differentiation of algebraic functions
- Exposure to multivariate calculus and partial derivatives
- Experience with formulating industrial and graph theoretical Problems using integer and linear programs
- Duality theory in linear programming
- Exposure to vectors and matrices.
Coordinator
Yu Hin (Gary) Au
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