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 ...
The DC programming and its DC algorithm (DCA) address the problem of minimizing a function f = g h (with g , h being lower semicontinuous proper convex functions on R n ) on the whole space. Based on local optimality conditions and DC duality, DCA was succ...
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 ...
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 ...
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 ...
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 ...
We improve this result by constructing a delta convex function of class $C^1(\Bbb R^2)$ which cannot be represented as a difference of two convex functions differentiable at 0. Further we give an example of a delta convex function differentiable everywhere which is not strictly differentiable...
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...
while retaining some of the advantages in working with convex functions in optimization, we propose a new neural network architecture called the CDiNN architecture. In this architecture, any given function that needs to be modeled is represented as a difference of convex functions. This enhances the...