A numerical study of an infeasible primal-dual path-following algorithm for linear programming

2007 ◽  
Vol 186 (2) ◽  
pp. 1472-1479 ◽  
Author(s):  
M. Achache ◽  
H. Roumili ◽  
A. Keraghel
Keyword(s):  
Linear Programming ◽  
Numerical Study ◽  
Path Following ◽  
Primal Dual ◽  
Path Following Algorithm

A relaxed primal-dual path-following algorithm for linear programming

1996 ◽  
Vol 62 (1) ◽  
pp. 173-196
Author(s):  
Tsung-Min Hwang ◽  
Chih-Hung Lin ◽  
Wen-Wei Lin ◽  
Shu-Cherng Fang
Keyword(s):  
Linear Programming ◽  
Path Following ◽  
Primal Dual ◽  
Path Following Algorithm

Fuzzy Heuristics for Sequential Linear Programming

1998 ◽  
Vol 120 (1) ◽  
pp. 17-23 ◽  
Author(s):  
E. L. Mulkay ◽  
S. S. Rao
Keyword(s):  
Fuzzy Logic ◽  
Linear Programming ◽  
Path Following ◽  
Human Observer ◽  
Sequential Linear Programming ◽  
Algorithm Performance ◽  
Primal Dual ◽  
Improve Algorithm ◽  
Range Of Values ◽  
Better Than

Numerical implementations of optimization algorithms often use parameters whose values are not strictly determined by the derivation of the algorithm, but must fall in some appropriate range of values. This work describes how fuzzy logic can be used to “control” such parameters to improve algorithm performance. This concept is shown with the use of sequential linear programming (SLP) due to its simplicity in implementation. The algorithm presented in this paper implements heuristics to improve the behavior of SLP based on current iterate values of design constraints and changes in search direction. Fuzzy logic is used to implement the heuristics in a form similar to what a human observer would do. An efficient algorithm, known as the infeasible primal-dual path-following interior-point method, is used for solving the sequence of LP problems. Four numerical examples are presented to show that the proposed SLP algorithm consistently performs better than the standard SLP algorithm.

Interior path following primal-dual algorithms. part I: Linear programming

1989 ◽  
Vol 44 (1-3) ◽  
pp. 27-41 ◽  
Author(s):  
Renato D. C. Monteiro ◽  
Ilan Adler
Keyword(s):  
Linear Programming ◽  
Path Following ◽  
Primal Dual

Plynomial Convergence of Primal-Dual Algorithms for SDLCP Based on the Monteiro-Zhang Family of Directions

2012 ◽  
Vol 532-533 ◽  
pp. 1857-1860
Author(s):  
Guang Zhou Li
Keyword(s):  
Complementarity Problems ◽  
Linear Complementarity Problems ◽  
Path Following ◽  
Complexity Bounds ◽  
New Class ◽  
Primal Dual ◽  
Carry Over ◽  
Path Following Methods ◽  
Finite Linear ◽  
Path Following Algorithm

The paper establishes the polynomial converge-nce of a new class of path-following methods for semide- finite linear complementarity problems (SDLCP) whos-se search directions belong to the class of directions introduced by Monteiro [7]. Namely, we show that the polynomial iteration complexity bounds of the well known algori-thm for linear programming, namely the short-step path-following algorithm of Kojima et al. and Monteiro and Alder, carry over to the context of SDLCP

A New Infinity-Norm Path Following Algorithm for Linear Programming

1995 ◽  
Vol 5 (2) ◽  
pp. 236-246 ◽  
Author(s):  
Kurt M. Anstreicher ◽  
Robert A. Bosch
Keyword(s):  
Linear Programming ◽  
Path Following ◽  
Infinity Norm ◽  
Path Following Algorithm
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