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Mathematical Optimization

Mathematical optimization gathers the convex analysis, duality theory, and nonlinear search strategies that underpin the rest of the control stack. Expect to find the tools needed to certify feasibility, reason about optimality conditions, and tune algorithms before they are deployed in MPC, optimal control, or reinforcement learning pipelines.

  1. Convexity Basics

    Learn the geometric and analytical tests for convex sets and functions used in optimization and control.

  2. Karush-Kuhn-Tucker Conditions

    Derive first-order optimality conditions for constrained problems and interpret multipliers geometrically.

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