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...
In particular, we focus on the optimization model introduced by Astorino and Gaudioso (J Optim Theory Appl 112(2):265–293, 2002) and adopt its reformulation in difference of convex (DC) form. We tackle the problem by adapting the algorithm for DC programming known as DCA. We present the...
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 ...
An approach to supervised distance metric learning based on difference of convex functions programming - bacnguyencong/DML-dc
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...
Use of Difference of Convex programming in decision making involving neural networks. • DC optimization produces better result at each iteration and guarantees convergence. • Optimization problem at each step reduces toLinear Programming problem. ...
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 ...
According to the 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 ...
(m_1,m_2) + 2ncannot be computed by standard abstract domains such asoctagonorpolyhedra: these domains areconvexand cannot express non-convex relations such asmaximum. The most precise approximation ofxin the polyhedra domain isx \le m_1 + m_2 + 2n. Unfortunately, it is well-known that...
Our compression model is formulated as an integer linear programming problem, which can be rewritten as a difference-of-convex (DC) programming problem based on the exact penalty technique. We use a well-known efficient DC algorithm—DCA to handle the penalized problem for local optimal solutions...