Results 71 to 80 of about 22,374 (199)

Incremental and Parallel Machine Learning Algorithms With Automated Learning Rate Adjustments

Frontiers in Robotics and AI, 2019
The existing machine learning algorithms for minimizing the convex function over a closed convex set suffer from slow convergence because their learning rates must be determined before running them.
Kazuhiro Hishinuma, Hideaki Iiduka
doaj   +1 more source

Approximate level method [PDF]

In this paper we propose and analyze a variant of the level method [4], which is an algorithm for minimizing nonsmooth convex functions. The main work per iteration is spent on 1) minimizing a piecewise-linear model of the objective function and on 2 ...
Richtarik, Peter
core  

A New Proximal Iteratively Reweighted Nuclear Norm Method for Nonconvex Nonsmooth Optimization Problems

Mathematics
This paper proposes a new proximal iteratively reweighted nuclear norm method for a class of nonconvex and nonsmooth optimization problems. The primary contribution of this work is the incorporation of line search technique based on dimensionality ...
Zhili Ge, Siyu Zhang, Xin Zhang, Yan Cui
doaj   +1 more source

A Decomposition Method with Redistributed Subroutine for Constrained Nonconvex Optimization

Abstract and Applied Analysis, 2013
A class of constrained nonsmooth nonconvex optimization problems, that is, piecewise C2 objectives with smooth inequality constraints are discussed in this paper.
Yuan Lu, Wei Wang, Li-Ping Pang, Dan Li
doaj   +1 more source

Nonsmooth analysis and optimization on partially ordered vector spaces

International Journal of Mathematics and Mathematical Sciences, 1992
Interval-Lipschitz mappings between topological vector spaces are defined and compared with other Lipschitz-type operators. A theory of generalized gradients is presented when both spaces are locally convex and the range space is an order complete vector
Thomas W. Reiland
doaj   +1 more source

Random gradient-free minimization of convex functions [PDF]

In this paper, we prove the complexity bounds for methods of Convex Optimization based only on computation of the function value. The search directions of our schemes are normally distributed random Gaussian vectors.
NESTEROV, Yurii
core  

Approximate Optimality Conditions for Nonsmooth Optimization Problems

Taiwanese Journal of Mathematics
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Son, Ta Quang, Bao, Hua Khac, Kim, Do Sang   +2 more
openaire   +1 more source

State Estimation and Tracking Control of Saturation Sandwich Systems Based on Particle Swarm Optimization

IEEE Access
This paper proposes an integrated control framework combining a nonsmooth observer and a switching feedback law to address the state estimation and tracking control challenges of sandwich systems with saturation nonlinearities.
Xufeng Liu, Zupeng Zhou
doaj   +1 more source

A two-point heuristic to calculate the stepsize in subgradient method with application to a network design problem

EURO Journal on Computational Optimization
We introduce a heuristic rule for calculating the stepsize in the subgradient method for unconstrained convex nonsmooth optimization which, unlike the classic approach, is based on retaining some information from previous iteration.
F. Carrabs, M. Gaudioso, G. Miglionico
doaj   +1 more source

Delayed Star Subgradient Methods for Constrained Nondifferentiable Quasi-Convex Optimization

Algorithms
In this work, we consider the problem of minimizing a quasi-convex function over a nonempty closed convex constrained set. In order to approximate a solution of the considered problem, we propose delayed star subgradient methods.
Ontima Pankoon, Nimit Nimana
doaj   +1 more source