Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
Artificial Intelligence Review - Special issue on lazy learning
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Approximating networks and extended Ritz method for the solution of functional optimization problems
Journal of Optimization Theory and Applications
Sufficient conditions for fast quasi-Monte Carlo convergence
Journal of Complexity
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Error Estimates for Approximate Optimization by the Extended Ritz Method
SIAM Journal on Optimization
Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
Deterministic design for neural network learning: an approach based on discrepancy
IEEE Transactions on Neural Networks
Deterministic Learning for Maximum-Likelihood Estimation Through Neural Networks
IEEE Transactions on Neural Networks
Local Models for data-driven learning of control policies for complex systems
Expert Systems with Applications: An International Journal
Adaptive value function approximation for continuous-state stochastic dynamic programming
Computers and Operations Research
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An approach based on semilocal approximation is introduced for the solution of a general class of operations research problems, such as Markovian decision problems, multistage optimal control, and maximum-likelihood estimation. Because it is extremely hard to derive analytical solutions that minimize the cost in most instances of the problem, we must look for approximate solutions. Here, it is shown that good solutions can be obtained with a moderate computational effort by exploiting properties of semilocal approximation through kernel models and efficient sampling of the state space. The convergence of the proposed method, called semilocal approximate minimization (SLAM), is discussed, and the consistency of the solution is derived. Simulation results show the efficiency of SLAM, also through its application to a classic operations research problem, i.e., inventory forecasting.