To overcome these challenges and enhance the accuracy of fluorescence distribution reconstruction, we propose a sparse reconstruction method based on dictionary learning via regularized orthogonal matching purs
Sequential equality-constrained optimization for nonlinear programming Article 03 May 2016 Convergence Analysis of Difference-of-Convex Algorithm with Subanalytic Data Article 26 July 2018 References Toland, J.F.: Duality in nonconvex optimization. J. Math. Anal. Appl. 66(2), 399–415 (1978...
DifferenceOfConvex(DC) 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,Pham...
Due to the use of the ramp loss function, the corresponding objective function is nonconvex, making it more challenging. To overcome this limitation, we formulate our distance metric learning problem as an instance of difference of convex functions (DC) programming. This allows us to design a ...
Paper tables with annotated results for Further properties of the forward-backward envelope with applications to difference-of-convex programming
Abstract 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...
An approach to supervised distance metric learning based on difference of convex functions programming - bacnguyencong/DML-dc
We offer an efficient approach based on difference of convex functions (DC) optimization for self-organizing maps (SOM). We consider SOM as an optimization problem with a nonsmooth, nonconvex energy function and investigated DC programming and DC algorithm (DCA), an innovative approach in nonconve...
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 ...
In this paper, we study optimality conditions for vector optimization problems of a difference of convex mappings (VP) {R-+(p)- - Minimize f (x)- g(x), subject to the constraints x is an element of C, l(x)is an element of- Q, Ax = b and h(x)- k(x) is an element of-...