Introduction to Dynamic ProgrammingBasic theory; Basic computations; Computational refinements; Risk, uncertainty, and competition; Nonserial systems; Infinite-stage systems. |
Contents
Introduction | 1 |
Basic Theory | 14 |
Recursive Equations for Final State and InitialFinal | 39 |
Copyright | |
43 other sections not shown
Common terms and phrases
assume C₁ C₂ calculations calculus of variations Chapter computations constraints convex function d₁ d₂ decision functions decision policy decision variables determine dimensionality discrete Dn(Xn dn+1 dynamic programming analysis Euler equation evaluations example expected return expected value feasible values Fibonacci search fn(Xn formulation given global optimum infinite-stage input integer k₁ k₂ kn+1 Lagrange multiplier lb/day loop max rn(Xn maximize maximum principle minimize minimum multistage N-stage n=1 N subject number of stages objective function obtain one-at-a-time method optimal decision optimal policy optimal return optimal solution optimization problem output P₁ payoff matrix Pn(kn probability pure strategies Qn(Xn r(dn r₁ random variables recursion equations recursive analysis return function returns and decisions rn(Xn search procedure serial system solve stage returns stage transformations stochastic storage requirements strategies subject to Xn-1 t₁ Table tion tn(Xn unimodal function V₁ variable problems x(t₁ x₁ y₁ yields