Abstract

We introduce a new parametric kernel function, which is a combination of the classic kernel function and a trigonometric barrier term, and present various properties of this new kernel function. A class of large- and small-update primal-dual interior-point methods for linear optimization based on this parametric kernel function is proposed. By utilizing the feature of the parametric kernel function, we derive the iteration bounds for large-update methods,O(n2/3log⁡(n/ε)), and small-update methods,O(nlog⁡(n/ε)). These results match the currently best known iteration bounds for large- and small-update methods based on the trigonometric kernel functions.

Highlights

  • In this paper, we consider the linear optimization (LO) problem in standard form min {cTx : Ax = b, x ≥ 0}, (P)where A ∈ Rm×n with rank(A) = m, b ∈ Rm, and c ∈ Rn

  • We introduce a new parametric kernel function, which is a combination of the classic kernel function and a trigonometric barrier term, and present various properties of this new kernel function

  • We develop some new properties of the parametric kernel function, as well as the corresponding barrier function

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Summary

Introduction

While the so-called small-update IPMs enjoy the best known worstcase iteration bounds but their performance in computational practice is poor This gap was reduced by Peng et al [2] who introduced the so-called self-regular kernel functions and designed primal-dual IPMs based on self-regular proximities for LO. El Ghami et al [6] first introduced a trigonometric kernel function for primal-dual IPMs in LO They established the worst case iteration bounds for largeand small-update methods, namely, O(n3/4 log(n/ε)) and O(√n log(n/ε)), respectively. Peyghami and Hafshejani [8] established the better iteration bound O(√n(log(n)) log(n/ε)) for large-update methods based on a new kernel function consisting of a trigonometric function in its barrier term.

Framework of Kernel-Based IPMs for LO
New Parametric Kernel Function and Its Properties
Analysis and Complexity of the Algorithms
Conclusions and Remarks
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