^The Boosted Difference of Convex Functions Algorithm for Nonsmooth Functionshttps://doi.org/10.1137/18M123339X
optimization, Bosted difference of convex... Learn more about optimization, dc-problem, mathematics Optimization Toolbox
Difference of convex functions algorithm (DCA)Linear preconditioning techniquesKurdyka–Łojasiewicz analysisWe consider the minimization problem with the truncated quadratic regularization, which is a nonsmooth and nonconvex problem. We cooperated the classical preconditioned iterations for linear equations into...
FunctionsandDCProgramming SongcanChen Outline 1.ABriefHistory 2.DCFunctionsandtheirProperty 3.Someexamples 4.DCProgramming 5.CaseStudy 6.Ournextwork 1.ABriefHistory •1964,HoangTuy,(incidentallyinhisconvex optimizationpaper), •1979,J.F.Toland,Dualityformulation •1985,PhamDinhTao,DCAlgorithm •...
characteristics of the spectrum for the indefinite kernel matrix, IKSVM-DC decomposes the objective function into the subtraction of two convex functions and thus reformulates the primal problem as a difference of convex functions (DC) programming which can be optimized by the DC algorithm (DCA)....
Through computational experiments, the efficacy of this methodology is demonstrated, surpassing traditional hyperparameter selection techniques. 展开 关键词: Support vector machines Computer science Numerical analysis Computational modeling Machine learning Vectors Convex functions Optimization Tuning Convergence ...
This paper studies the difference-of-convex (DC) penalty formulations and the associated difference-of-convex algorithm (DCA) for computing stationary solutions of linear programs with complementarity constraints (LPCCs). We focus on three such formulations and establish connections between their stationar...
For the problem of minimizing a lower semicontinuous proper convex function f on a Hilbert space, the proximal point algorithm in exact form generates a se... ROCKAFELLAR,R. T. - 《Siam Journal on Control & Optimization》 被引量: 4659发表: 1976年 Interactive Multiobjective Optimization Procedur...
Given a nonconvex function f defined as the difference of two convex functions g and h (f is a so-called d.c. function), we study the regularized (or smoothed) version of f obtained by performing the infimal convolution of both component functions g and h by the same kernel function ....
However, the performance of AdaGrad is deteriorated with dense gradients and nonconvex objective functions [31]. Consequently, we explore the integration of neural TD with Adam-type algorithms, which stand out as more potent tools for training deep neural networks owing to their superior performance...