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Pontryagin's maximum principle
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Related lectures (12)
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Optimal Control: NMPC
Covers Nonlinear Model Predictive Control (NMPC) principles, including setpoint stabilization and Pontryagin's Maximum Principle.
Optimal Control: OCPs
Covers Optimal Control Problems focusing on necessary conditions, existence of optimal controls, and numerical solutions.
Optimal Control Theory: Basics
Covers the fundamentals of optimal control theory, focusing on defining OCPs, existence of solutions, performance criteria, physical constraints, and the principle of optimality.
Optimal Control Theory: OCPs
Covers Optimal Control Theory, focusing on Optimal Control Problems (OCPs) and the calculus of variations.
Hamilton-Jacobi Equation: Exact Fields
Explores the Hamilton-Jacobi equation, optimal control, and Bellman principle in exact fields.
Newton's Method: Solving Optimality Conditions
Covers Newton's method for optimizing problems, its limitations, and adaptations.
Dynamic Programming: Optimal Control
Explores Dynamic Programming for optimal control, focusing on stability, stationary policy, and recursive solutions.
Stationary Points and Lagrange Multiplier Theorem
Explores stationary points and the Lagrange multiplier theorem for function optimization.
Constrained optimization: the basics
Covers the basics of constrained optimization, including tangent directions, trust-region subproblems, and necessary optimality conditions.
Optimal Control: Unconstrained and Constrained Problems
Explores optimal control in unconstrained and constrained problems, emphasizing the importance of sparsity.
Numerical Simulation of SDEs: Monte Carlo & Optimal Control
Covers Monte Carlo methods, variance reduction, and stochastic optimal control, exploring simulation techniques, efficiency, and investment dynamics.
Dynamic Games: Backward Induction and Nash Equilibria
Covers dynamic games, focusing on backward induction and finding Nash equilibria in two-player scenarios.
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