Dynamic Programming and the Calculus of VariationsDreyfus Dynamic Programming and the Calculus of Variations |
Contents
1 | |
Chapter II The Classical Variational Theory | 25 |
Chapter III The Simplest Problem | 69 |
Chapter IV The Problem of Mayer | 129 |
Chapter V Inequality Constraints | 164 |
Chapter VI Problems with Special Linear Structures | 190 |
Chapter VII Stochastic and Adaptive Optimization Problems | 205 |
Bibliography | 242 |
245 | |
246 | |
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Common terms and phrases
absolute minimality admissible curves arc-length associated boundary condition calculus of variations Chapter classical computed consider control function control policy criterion function decision deduced defined definite integral determined deterministic dynamic programming end point equals zero Euler curve Euler–Lagrange equation evaluated example expected value function S(x fundamental equation fundamental partial differential given inequality constraint initial point Jacobi condition Legendre condition minimizing curve minimum value multiplier functions multiplier rule necessary condition obtain optimal control optimal curve optimal path optimal policy function optimal trajectory optimal value function ordinary differential equations partial differential equation particular perturbed problem discontinuity properties recurrence relation region relative minimality result satisfied second partial derivatives shown in Fig simplest problem solution curve Sºy specified stochastic terminal conditions terminal control problem terminal manifold terminal point terminal value tion Transversality Conditions variational problem vector vertex weak neighborhood Weierstrass yields