Interior-point method for large-scale $l_1$ optimization

Lukšan, Ladislav; Matonoha, Ctirad; Vlček, Jan

Displaying similar documents to “Interior-point method for large-scale $l_{1}$ optimization”

Saddle point criteria for second order $η$ -approximated vector optimization problems

Anurag Jayswal, Shalini Jha, Sarita Choudhury (2016)

Kybernetika

Similarity:

The purpose of this paper is to apply second order $η$ -approximation method introduced to optimization theory by Antczak [2] to obtain a new second order $η$ -saddle point criteria for vector optimization problems involving second order invex functions. Therefore, a second order $η$ -saddle point and the second order $η$ -Lagrange function are defined for the second order $η$ -approximated vector optimization problem constructed in this approach. Then, the equivalence between an (weak) efficient solution...

The general methods of finding the sum $S_{n}$ for all kinds of series, II

K. Orlov (1981)

Matematički Vesnik

Similarity:

Localization techniques in $L^{p}$ spaces

A. Pełczyński, H. Rosenthal (1975)

Studia Mathematica

Similarity:

Optimization problem under two-sided (max, +)/(min, +) inequality constraints

Karel Zimmermann (2020)

Applications of Mathematics

Similarity:

$(max, +)$ -linear functions are functions which can be expressed as the maximum of a finite number of linear functions of one variable having the form $f (x_{1}, \dots, x_{h}) = {max}_{j} (a_{j} + x_{j})$ , where $a_{j}$ , $j = 1, \dots, h$ , are real numbers. Similarly $(min, +)$ -linear functions are defined. We will consider optimization problems in which the set of feasible solutions is the solution set of a finite inequality system, where the inequalities have $(max, +)$ -linear functions of variables $x$ on one side and $(min, +)$ -linear functions of variables $y$ on the other side....