AN ELIGIBLE KERNEL BASED PRIMAL-DUAL INTERIOR-POINT METHOD FOR LINEAR OPTIMIZATION
نویسندگان
چکیده
منابع مشابه
Primal-dual entropy-based interior-point algorithms for linear optimization
We propose a family of search directions based on primal-dual entropy in the contextof interior-point methods for linear optimization. We show that by using entropy based searchdirections in the predictor step of a predictor-corrector algorithm together with a homogeneousself-dual embedding, we can achieve the current best iteration complexity bound for linear opti-mization. The...
متن کاملA primal-dual interior-point method based on a new kernel function with linear growth rate
We introduce a new barrier function which has a linear growth term in its kernel function. So far all existing kernel functions have a quadratic (or higher degree) growth term. Despite this, a large-update primal-dual interior-point method based on this kernel function has the same iteration bound as the classical primal-dual method, which is based on the logarithmic barrier method.
متن کاملA primal-dual interior-point method for linear optimization based on a new proximity function
In this paper we present a generic primal-dual interior-point algorithm for linear optimization in which the search direction depends on a univariate kernel function which is also used as proximity measure in the analysis of the algorithm. We present some powerful tools for the analysis of the algorithm under the assumption that the kernel function satisfies three easy to check and mild conditi...
متن کاملA Comparative Study of Kernel Functions for Primal-Dual Interior-Point Algorithms in Linear Optimization
Recently, so-called self-regular barrier functions for primal-dual interior-point methods (IPMs) for linear optimization were introduced. Each such barrier function is determined by its (univariate) self-regular kernel function. We introduce a new class of kernel functions. The class is defined by some simple conditions on the kernel function and its derivatives. These properties enable us to d...
متن کاملPrimal-Dual Interior-Point Algorithms for Semidefinite Optimization Based on a Simple Kernel Function
Interior-point methods (IPMs) for semidefinite optimization (SDO) have been studied intensively, due to their polynomial complexity and practical efficiency. Recently, J.Peng et al. [14, 15] introduced so-called self-regular kernel (and barrier) functions and designed primal-dual interior-point algorithms based on self-regular proximity for linear optimization (LO) problems. They have also exte...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Honam Mathematical Journal
سال: 2013
ISSN: 1225-293X
DOI: 10.5831/hmj.2013.35.2.235