Computational Aspects of Linear Programming

1996 ◽  
pp. 169-193
Author(s):  
Michael J. Panik
Keyword(s):  
Linear Programming ◽  
Computational Aspects

Computational aspects of linear programming Simplex method

2000 ◽  
Vol 31 (8-9) ◽  
pp. 539-545 ◽  
Author(s):  
D.T. Nguyen ◽  
Y. Bai ◽  
J. Qin ◽  
B. Han ◽  
Y. Hu
Keyword(s):  
Linear Programming ◽  
Simplex Method ◽  
Computational Aspects

An Extended Programming Experience

1981 ◽  
Vol 13 (1) ◽  
pp. 19-27 ◽  
Author(s):  
B Harris
Keyword(s):  
Linear Programming ◽  
Transportation Problem ◽  
Dual Methods ◽  
Solution Methods ◽  
Research Problems ◽  
Computational Aspects ◽  
Standard Solution ◽  
Order Bounds

The paper summarizes a twenty-year experience with the computational aspects of the transportation problem of linear programming. The problem is completely described and standard solution methods are summarized. The order bounds for these computations are examined. Attention then turns to some sample research problems, including shortening the search for a move, solving a condensed problem, choosing a starting basis, and several recently developed dual methods. Brief reference is made to the difficulties which arise in publishing unorthodox approaches such as those discussed in the paper.

scholarly journals Solving Factored MDPs with Hybrid State and Action Variables

2006 ◽  
Vol 27 ◽  
pp. 153-201 ◽  
Author(s):  
B. Kveton ◽  
M. Hauskrecht ◽  
C. Guestrin
Keyword(s):  
Linear Programming ◽  
Optimization Problems ◽  
Scale Up ◽  
Efficient Solutions ◽  
Compact Representation ◽  
Optimal Value ◽  
Markov Decision ◽  
Computational Aspects ◽  
Approximate Linear Programming ◽  
Continuous And Discrete Variables

Efficient representations and solutions for large decision problems with continuous and discrete variables are among the most important challenges faced by the designers of automated decision support systems. In this paper, we describe a novel hybrid factored Markov decision process (MDP) model that allows for a compact representation of these problems, and a new hybrid approximate linear programming (HALP) framework that permits their efficient solutions. The central idea of HALP is to approximate the optimal value function by a linear combination of basis functions and optimize its weights by linear programming. We analyze both theoretical and computational aspects of this approach, and demonstrate its scale-up potential on several hybrid optimization problems.

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