We first develop an equivalent Difference of Convex functions (DC) representation for the non-convex Boolean constraint imposed on the association variables, making the problem tractable. Then, a DC algorithm is derived to efficiently solve the resulting optimization problem. Simulation results ...
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^The Boosted Difference of Convex Functions Algorithm for Nonsmooth Functionshttps://doi.org/10.1137/18M123339X
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 •...
Souza, J.C.O., Oliveira, P.R., Soubeyran, A.: Global convergence of a proximal linearized algorithm for difference of convex functions. Optim. Lett. 10(7), 1529–1539 (2016) Article MathSciNet Google Scholar Clarke, F.: Optimisation and nonsmooth analysis. Classics in applied mathematic...
Computation of PhaseLiftOff minimization is carried out by a convergent difference of convex functions algorithm. In our numerical example, $a_i$'s are Gaussian distributed. Numerical results show that PhaseLiftOff outperforms PhaseLift and its nonconvex variant (log-determinant regularization), and ...
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...
Two new penalty methods for sparse reconstruction are proposed based on two types of difference of convex functions (DC for short) programming in which the DC objective functions are the difference of l1 and lσ q norms and the difference of l1 and lr norms with r > 1. By introducing a ...
convex CPWL function as the pointwise-maximum over a set of affine functions, the DC CPWL representation enables the polytope regions defining a CPWL function to be implicitly defined by the affine functions making up the convex components. By searching the affine functions of the convex ...