Standard syllabus
Optimization for CS · Graduate · CS / Programming
Topics
Convex optimization
- Convex sets and functions; optimality conditions
- Linear programming and simplex overview
- Gradient descent and projected gradient
- Lagrangian duality and KKT conditions
- Second-order cone and semidefinite programs (intro)
Discrete and stochastic
- Integer programming and branch-and-bound (intro)
- Network flow as optimization
- Subgradient methods for non-smooth problems
- Stochastic gradient descent and mini-batching
- Online convex optimization (intro)
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.