Results 161 to 170 of about 2,178 (210)

Selective Inference for Sparse Graphs via Neighborhood Selection. [PDF]

Electron J Stat
Huang Y, Panigrahi S, Dempsey W.
europepmc   +1 more source

A necessary condition for the guarantee of the superiorization method. [PDF]

Optim Lett
Barshad K, Censor Y, Moursi W, Weames T, Wolkowicz H.   +4 more
europepmc   +1 more source

A DECOMPOSITION ALGORITHM FOR TWO-STAGE STOCHASTIC PROGRAMS WITH NONCONVEX RECOURSE FUNCTIONS. [PDF]

SIAM J Optim
Li H, Cui Y.
europepmc   +1 more source

Sparse regularization inversion method for transient electromagnetic data and high-resolution prospection of subsurface targets. [PDF]

Sci Rep
Zhou Z, He K, Kang S, Zhang J, Chi F, Ren J, Zhang W, Ning C.   +7 more
europepmc   +1 more source

On Convergence Properties of a Subgradient Method [PDF]

Optimization Methods and Software, 2003
In this article, we consider convergence properties of the normalized subgradient method which employs the stepsize rule based on a priori knowledge of the optimal value of the cost function. We show that the normalized subgradients possess additional information about the problem under consideration, which can be used for improving convergence rates ...
I V Konnov
exaly   +5 more sources

An Effective Line Search for the Subgradient Method

Journal of Optimization Theory and Applications, 2005
One of the main drawbacks of the subgradient method is the tuning process to determine the sequence of steplengths. In this paper, the radar subgradient method, a heuristic method designed to compute a tuning-free subgradient steplength, is geometrically motivated and algebraically deduced.
Beltran, C, Heredia, F.-Javier (Francisco Javier)   +1 more
exaly   +5 more sources

Convergence of a generalized subgradient method for nondifferentiable convex optimization

Mathematical Programming, 1991
A generalized subgradient method is considered which uses the subgradients at previous iterations as well as the subgradient at current point. This method is a direct generalization of the usual subgradient method.
Kim, Sehun, AHN, H
exaly   +1 more source

A system of nonsmooth equations solver based upon subgradient method

Applied Mathematics and Computation, 2015
In this paper, a subgradient method is developed to solve the system of (nonsmooth) equations. First, the system of (nonsmooth) equations is transformed into a nonsmooth optimization problem with zero minimal objective function value. Then, a subgradient
Qiang Long, Changzhi Wu, Xiangyu Wang
exaly   +2 more sources

Stochastic Subgradient Method Converges on Tame Functions [PDF]

Foundations of Computational Mathematics, 2019
This work considers the question: what convergence guarantees does the stochastic subgradient method have in the absence of smoothness and convexity? We prove that the stochastic subgradient method, on any semialgebraic locally Lipschitz function, produces limit points that are all first-order stationary.
Damek Davis, Dmitriy Drusvyatskiy, Jason D Lee   +2 more
exaly   +4 more sources