On the Complexity of a Linear Programming Predictor-Corrector Algorithm Using the Two Norm Neighborhood

被引：0
|
作者：
Teixeira, Ana [1 ,2 ]
Almeida, R. [1 ,2 ]
机构：
[1] Univ Minho Pole CMAT UTAD, Ctr Math, Braga, Portugal
[2] Univ Tras Os Montes & Alto Douro, Dept Math, P-5000801 Vila Real, Portugal
来源：
OPTIMIZATION, LEARNING ALGORITHMS AND APPLICATIONS, OL2A 2022 | 2022年 / 1754卷
关键词：
Feasible predictor-corrector algorithm; Transportation and assignment problems; Polynomial complexity; INTERIOR-POINT ALGORITHMS; CONVERGENCE ANALYSIS; POLYNOMIALITY;
D O I：
10.1007/978-3-031-23236-7_37
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this work the complexity of a feasible variant of a Linear Programming Predictor-Corrector algorithm specialized for transportation and assignment problems is explored. A O(n| log(epsilon)|) iteration complexity was achieved by proving that the step size computed by the studied algorithm is bounded at each iteration by theta(4-3 theta)(1-theta)(2)/n, where theta is an element of [0, 1[. Therefore, allowing to conclude that the analyzed Predictor-Corrector algorithm that uses the 2-norm neighborhood has polynomial iteration complexity and is Q-linearly convergent.
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页码：533 / 545
页数：13
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