An Elementary Introduction to Dynamic Programming: A State Equation Approach"When the Japanese landed at Rabaul on Friday 23 January 1942 it was the start of one of the fiercest campaigns of the war. On that day, with only a handful of badly trained troops led by inexperienced officers, with a civil administration torn with incompetence and jealousies, Australia faced its most serious threat yet. For Australia itself was one of the most important targets"--Jacket. |
Contents
Introduction | 1 |
An InvestmentAllocation Problem | 6 |
A Simple Dynamic Optimization Problem and Its Relationship | 23 |
Copyright | |
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Common terms and phrases
analogously to Eq analytical assume Bayesian Bayesian probabilities boundary C₁ chapter choose computationally computer storage consider constant stock level corresponding decisions v₁ defined determine deterministic dimensionality discrete search discussion dynamic programming example fi(x final finite fo(x functional equations g(xo gambler's ruin gives grid H₁ H₂ increases induction infinite-stage process initial state xo integer inventory Lagrange multipliers mathematical Max h(x Max v² maximize methods minimize module monotonically multi-stage decision processes N-stage process notation Note obtain optimal decisions optimal policy p₁ parameters particle path criterion function possible principle of optimality probability problem process starting random variable random walk reader respectively result right-hand side sequential sequential analysis Similarly simulation solve stage stochastic stochastic processes sub-optimal successive approximations Suppose tables tions v₁² v₂ v₂² values variable Vs+1 vz² x-range x₁ Xs+1 zero