We address a large class of Mathematical Programs with Linear Complementarity Constraints which minimizes a continuously differentiable DC function (Difference of Convex functions) on a set defined by linear constraints and linear complementarity constraints, named Difference of Convex functions programs with...
function J 2m (T,V) is concave in variable T (since H(T,V ) is convex). Hence S (sphere, i.e., boundary) contains minimizers (reaching at boundary) of J 2m (T,V ) on B, i.e., DC Formulation where Solving FCM by DCA (1) A key: construct two sequences (Y l , Z ...
今天介绍的这种算法,Difference of Convex Algorithm,也就是DCA,以便咱们解决非凸优化问题。 但这种问题仅仅针对某些特定情况。 首先,咱们对这串英文进行解读,Difference理解为差,convex是凸,即凸函数,algorithm嘛,你们自己查词典,哈哈哈。所以呢,DCA就是两个凸函数的差的算法。 你们肯定懵逼了,啥凸函数的差?Emmm, ...
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 ...
Difference of quasiconvex functionsQ-subdifferentialOptimality conditionsIn this note, we are concerned with an optimization problem (P) where the objective function is the difference of two quasiconvex functions. Using a suitable subdifferential introduced by Suzuki and Kuroiwa (Nonlinear Anal 74:1279–...
In this paper, we consider a class of difference-of-convex (DC) optimization problems, which require only a weaker restrictedL-smooth adaptable property on the smooth part of the objective function, instead of the standard global Lipschitz gradient continuity assumption. Such problems are prevalent ...
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
This study proposes a bilevel optimization schema tailored for hyperparameter selection in SVR, subsequently recasting the problem into a Difference of Convex functions model (DC). We delineate convergence outcomes for this approach. Through computational experiments, the efficacy of this methodology is ...
1992: New types of variational principles based on the notion of quasidifferentiability.Acta Mech. 94, 171–194 Polyakova, L.N. 1986: On minimizing the sum of a convex function and a concave function.Math. Prog. Study 19, 69–73 Rozvany, G.I.N. 1970: Concave programming in ...