Nonlinear optimization
Optimization & linear programming · STEM / applied
Unconstrained optimization: critical points and convexity
Objectives
- Unconstrained optimization: critical points and convexity
- Gradient descent and Newton's method for multivariable functions
- Constrained optimization: Lagrange multipliers
- Karush–Kuhn–Tucker (KKT) conditions (introduction)
- Convex sets and convex functions (definitions and examples)
Study materials
- Practice testComing soon