Control of Diffusions via Linear Programming
This chapter presents an approach that leverages linear programming to approximate optimal policies for controlled diffusion processes, possibly with high-dimensional state and action spaces. The approach fits a linear combination of basis functions to the dynamic programming value function; the resulting approximation guides control decisions. Linear programming is used here to compute basis function weights. This builds on the linear programming approach to approximate dynamic programming, previously developed in the context of discrete-time stochastic control.
KeywordsPortfolio Optimization Convex Quadratic Program Linear Programming Approach Portfolio Optimization Problem Portfolio Strategy
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