Design of algorithms for spatial-time reduction complexity of dynamic programming

1992 ◽  
Vol 139 (2) ◽  
pp. 172 ◽  
Author(s):  
L. Moreno ◽  
L. Acosta ◽  
J.L. Sánchez
Keyword(s):  
Dynamic Programming ◽  
Design Of Algorithms ◽  
Time Reduction

scholarly journals Deep Neural Network Approximated Dynamic Programming for Combinatorial Optimization

2020 ◽  
Vol 34 (02) ◽  
pp. 1684-1691
Author(s):  
Shenghe Xu ◽  
Shivendra S. Panwar ◽  
Murali Kodialam ◽  
T.V. Lakshman
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Dynamic Programming ◽  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Computation Time ◽  
Training Procedure ◽  
Problem Size ◽  
Combinatorial Optimization Problems ◽  
Time Reduction

In this paper, we propose a general framework for combining deep neural networks (DNNs) with dynamic programming to solve combinatorial optimization problems. For problems that can be broken into smaller subproblems and solved by dynamic programming, we train a set of neural networks to replace value or policy functions at each decision step. Two variants of the neural network approximated dynamic programming (NDP) methods are proposed; in the value-based NDP method, the networks learn to estimate the value of each choice at the corresponding step, while in the policy-based NDP method the DNNs only estimate the best decision at each step. The training procedure of the NDP starts from the smallest problem size and a new DNN for the next size is trained to cooperate with previous DNNs. After all the DNNs are trained, the networks are fine-tuned together to further improve overall performance. We test NDP on the linear sum assignment problem, the traveling salesman problem and the talent scheduling problem. Experimental results show that NDP can achieve considerable computation time reduction on hard problems with reasonable performance loss. In general, NDP can be applied to reducible combinatorial optimization problems for the purpose of computation time reduction.

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