MSML604
Introduction to Optimization
Prerequisite: undergraduate courses in calculus and basic linear algebra. The course focuses on recognizing, solving, and analyzing optimization problems. Linear algebra overview: vector spaces and matrices, linear transformations, matrix algebra, projections, similarity transformations, norms, eigen-decomposition and SVD. Convex sets, convex functions, duality theory and optimality conditions. Unconstrained optimization: 1D search, steepest descent, Newton's method, conjugate gradient method, DFP and BFGS methods, stochastic gradient descent. Constrained optimization: projected gradient methods, linear programming, quadratic programming, penalty functions, and interior-point methods. Global search methods: simulated annealing, genetic algorithms, particle swarm optimization.
Spring 2026
0 reviews
Average rating:
N/A
20 reviews
Average rating:
3.35
0 reviews
Average rating:
N/A
Spring 2025
20 reviews
Average rating:
3.35
Past Semesters
20 reviews
Average rating:
3.35
20 reviews
Average rating:
3.35
1 review
Average rating:
4.00
20 reviews
Average rating:
3.35