Results 91 to 100 of about 1,615 (176)
A golden ratio technique for equilibrium problem in reflexive Banach spaces
Demonstratio MathematicaIn this article, we present a self-adaptive subgradient extragradient method for approximating solutions of equilibrium problems with pseudomonotone and Lipschitz type bifunctions in the context of reflexive Banach spaces.
Abass Hammed A. +3 more
doaj +1 more source
Diagnosis of Alzheimer's Disease Based on Accelerated Mirror Descent Optimization and a Three-Dimensional Aggregated Residual Network. [PDF]
Sensors (Basel), 2023 Tu Y, Lin S, Qiao J, Zhang P, Hao K.
europepmc +1 more source
Quasi-monotone subgradient methods for nonsmooth convex minimization
, 2015 In this paper, we develop new subgradient methods for solving nonsmooth convex optimization problems. These methods guarantee the best possible rate of convergence for the whole sequence of test points.
Nesterov, Yurii, Shikhman, Vladimir
core +1 more source
(Online) Subgradient Methods for Structured Prediction
, 2018 Promising approaches to structured learning problems have recently been developed in the maximum margin framework. Unfortunately, algorithms that are computationally and memory efficient enough to solve large scale problems have lagged behind.
Nathan D. Ratliff (5398466) +2 more
core +1 more source
Control learning rate for autism facial detection via deep transfer learning. [PDF]
Signal Image Video Process, 2023 El Mouatasim A, Ikermane M.
europepmc +1 more source
Regret Function Minimization Algorithms
Кібернетика та комп'ютерні технологіїIntroduction. The article addresses the problem of making optimal decisions under uncertainty by minimizing the Savage regret function. This function, which evaluates the difference between the actual outcome and the best possible outcome across all ...
Anatolie Baractari +2 more
doaj +1 more source
Ergodic Results In Subgradient Optimization
, 1995 : Subgradient methods are popular tools for nonsmooth, convex minimization, especially in the context of Lagrangean relaxation; their simplicity has been a main contribution to their success.
Ann-Brith Strömberg +5 more
core
R-algorithm for Solving Quadratic Programming Problems
Кібернетика та комп'ютерні технологіїQuadratic programming problems have a wide range of practical applications in various fields of science and engineering, particularly in financial modeling and pattern recognition, which underscores the relevance of studying methods for their efficient ...
Petro Stetsyuk +3 more
doaj +1 more source
Surrogate "Level-Based" Lagrangian Relaxation for mixed-integer linear programming. [PDF]
Sci Rep, 2022 Bragin MA, Tucker EL.
europepmc +1 more source
Primal-dual subgradient methods for convex problems
In this paper we present a new approach for constructing subgradient schemes for different types of nonsmooth problems with convex structure. Our methods are primaldual since they are always able to generate a feasible approximation to the optimum of an ...
NESTEROV, Yu.
core