Results 261 to 270 of about 10,265,072 (333)

A boosted DC algorithm for non-differentiable DC components with non-monotone line search

Computational optimization and applications, 2021
We introduce a new approach to apply the boosted difference of convex functions algorithm (BDCA) for solving non-convex and non-differentiable problems involving difference of two convex functions (DC functions).
O. P. Ferreira, Elianderson M. Santos, J. C. O. Souza   +2 more
semanticscholar   +1 more source

A Stochastic Line Search Method with Expected Complexity Analysis

SIAM Journal on Optimization, 2020
For deterministic optimization, line search methods augment algorithms by providing stability and improved efficiency.
Courtney Paquette, K. Scheinberg
semanticscholar   +1 more source

The PRP conjugate gradient algorithm with a modified WWP line search and its application in the image restoration problems

Applied Numerical Mathematics, 2020
It is well known that the conjugate gradient algorithm is one of the most classic and useful methods for solving large-scale optimization problems, where the Polak-Ribiere-Polyak(PRP) method is an important and effective conjugate gradient algorithm ...
Gonglin Yuan, Junyu Lu, Zhan Wang
semanticscholar   +1 more source

The convergence properties of RMIL+ conjugate gradient method under the strong Wolfe line search

Applied Mathematics and Computation, 2020
In Dai (2016), based on the global convergence of RMIL conjugate gradient method, Dai has modified it and called the modified version RMIL+ which has good numerical results and globally convergent under the exact line search.
O. Yousif
semanticscholar   +1 more source

Non-asymptotic global convergence rates of BFGS with exact line search

Mathematical programming
In this paper, we explore the non-asymptotic global convergence rates of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method implemented with exact line search.
Qiujiang Jin, Ruichen Jiang, Aryan Mokhtari   +2 more
semanticscholar   +1 more source

Parallel line search

2009
We consider the well-known line search algorithm that iteratively refines the search interval by subdivision and bracketing the optimum. In our applications, evaluations of the objective function typically require minutes or hours, so it becomes attractive to use more than the standard three steps in the subdivision, performing the evaluations in ...
Peachey, T. C., Abramson, D., Lewis, A.
openaire   +2 more sources

Non-asymptotic Global Convergence Analysis of BFGS with the Armijo-Wolfe Line Search

Neural Information Processing Systems
In this paper, we present the first explicit and non-asymptotic global convergence rates of the BFGS method when implemented with an inexact line search scheme satisfying the Armijo-Wolfe conditions. We show that BFGS achieves a global linear convergence
Qiujiang Jin, Ruichen Jiang, Aryan Mokhtari   +2 more
semanticscholar   +1 more source

A Wolfe Line Search Algorithm for Vector Optimization

ACM Transactions on Mathematical Software, 2019
In a recent article, Lucambio Pérez and Prudente extended the Wolfe conditions for the vector-valued optimization. Here, we propose a line search algorithm for finding a step size satisfying the strong Wolfe conditions in the vector optimization setting.
L. R. L. Pérez, L. F. Prudente
semanticscholar   +1 more source

Variance-Based Extragradient Methods with Line Search for Stochastic Variational Inequalities

SIAM Journal on Optimization, 2019
In this paper, we propose dynamic sampled stochastic approximated (DS-SA) extragradient methods for stochastic variational inequalities (SVIs) that are robust with respect to an unknown Lipschitz c...
A. Iusem, A. Jofré, R. Oliveira, Philip Thompson   +3 more
semanticscholar   +1 more source

Stochastic quasi-Newton with line-search regularization

at - Automatisierungstechnik, 2019
In this paper we present a novel quasi-Newton algorithm for use in stochastic optimisation. Quasi-Newton methods have had an enormous impact on deterministic optimisation problems because they afford rapid convergence and computationally attractive ...
A. Wills, Thomas Bo Schön
semanticscholar   +1 more source