Optimization and Dynamic Programming
Optimization chooses controls to improve an explicit objective under constraints. Dynamic programming decomposes sequential decisions through state and future value.
Central questions
- What state is sufficient for future decisions?
- Which costs, constraints, and terminal conditions define success?
- When does the state space make exact recursion impractical?
Planned model
Solve a finite-horizon inventory liquidation problem by backward induction and expose each state value, action choice, and sensitivity to assumptions.