Add to ORKGA large number of economic decision problems are naturally expressed as stochastic dynamic programs (SDPs).1 These problems are intrinsically high dimensional, and quantitative researchers have devoted a vast amount of com-puter time to solving them numerically over the past few decades. While com
A stagewise decomposition algorithm called ???value function gradient learning??? (VFGL) is proposed...
A standard problem in the field of computational finance is that of pricing derivative securities. T...
Computing the exact solution of an MDP model is generally difficult and possibly intractable for rea...
This text gives a comprehensive coverage of how optimization problems involving decisions and uncert...
Title: Stochastic Dynamic Programming Problems: Theory and Applications Author: Gabriel Lendel Depar...
Stochastic dynamic programming (SDP) is a useful tool for analyzing policy questions in fisheries ma...
Dynamic programming (DP) is one of the most important mathematical programming methods. However, a m...
Value function iteration is one of the Standard tools for the solution of dynamic general equilibriu...
This paper develops a new method for constructing approximate solutions to discrete time, infinite h...
A classic solution technique for Markov decision processes (MDP) and stochastic games (SG) is value ...
AbstractStochastic dynamic programs suffer from the so called curse of dimensionality whereby the nu...
We propose an alternative approach to stochastic programming based on Monte-Carlo sampling and stoch...
A stagewise decomposition algorithm called value function gradient learning (VFGL) is proposed for l...
A heuristic algorithm is proposed for a class of stochastic discrete-time continuous-variable dynami...
Stochastic dynamic programming (SDP) is a useful tool for analyzing policy questions in fisheries ma...
A stagewise decomposition algorithm called ???value function gradient learning??? (VFGL) is proposed...
A standard problem in the field of computational finance is that of pricing derivative securities. T...
Computing the exact solution of an MDP model is generally difficult and possibly intractable for rea...
This text gives a comprehensive coverage of how optimization problems involving decisions and uncert...
Title: Stochastic Dynamic Programming Problems: Theory and Applications Author: Gabriel Lendel Depar...
Stochastic dynamic programming (SDP) is a useful tool for analyzing policy questions in fisheries ma...
Dynamic programming (DP) is one of the most important mathematical programming methods. However, a m...
Value function iteration is one of the Standard tools for the solution of dynamic general equilibriu...
This paper develops a new method for constructing approximate solutions to discrete time, infinite h...
A classic solution technique for Markov decision processes (MDP) and stochastic games (SG) is value ...
AbstractStochastic dynamic programs suffer from the so called curse of dimensionality whereby the nu...
We propose an alternative approach to stochastic programming based on Monte-Carlo sampling and stoch...
A stagewise decomposition algorithm called value function gradient learning (VFGL) is proposed for l...
A heuristic algorithm is proposed for a class of stochastic discrete-time continuous-variable dynami...
Stochastic dynamic programming (SDP) is a useful tool for analyzing policy questions in fisheries ma...
A stagewise decomposition algorithm called ???value function gradient learning??? (VFGL) is proposed...
A standard problem in the field of computational finance is that of pricing derivative securities. T...
Computing the exact solution of an MDP model is generally difficult and possibly intractable for rea...