Dynamic optimization and its relation to classical and quantum constrained systems
Rely Pellicer and
Physica A: Statistical Mechanics and its Applications, 2017, vol. 479, issue C, 12-25
We study the structure of a simple dynamic optimization problem consisting of one state and one control variable, from a physicist’s point of view. By using an analogy to a physical model, we study this system in the classical and quantum frameworks. Classically, the dynamic optimization problem is equivalent to a classical mechanics constrained system, so we must use the Dirac method to analyze it in a correct way. We find that there are two second-class constraints in the model: one fix the momenta associated with the control variables, and the other is a reminder of the optimal control law. The dynamic evolution of this constrained system is given by the Dirac’s bracket of the canonical variables with the Hamiltonian. This dynamic results to be identical to the unconstrained one given by the Pontryagin equations, which are the correct classical equations of motion for our physical optimization problem. In the same Pontryagin scheme, by imposing a closed-loop λ-strategy, the optimality condition for the action gives a consistency relation, which is associated to the Hamilton–Jacobi–Bellman equation of the dynamic programming method. A similar result is achieved by quantizing the classical model. By setting the wave function Ψ(x,t)=eiS(x,t) in the quantum Schrödinger equation, a non-linear partial equation is obtained for the S function. For the right-hand side quantization, this is the Hamilton–Jacobi–Bellman equation, when S(x,t) is identified with the optimal value function. Thus, the Hamilton–Jacobi–Bellman equation in Bellman’s maximum principle, can be interpreted as the quantum approach of the optimization problem.
Keywords: Dynamic optimization; Constrained systems; Dirac’s method; Quantum mechanics (search for similar items in EconPapers)
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Working Paper: Dynamic optimization and its relation to classical and quantum constrained systems (2016)
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