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 b
(2014). Feature Selection in machine learning: an exact penalty approach using a Difference of Convex function Algorithm, submitted.Le Thi HA, Le HM, Pham Dinh T Feature selection in machine learning: an exact penalty approach using a difference of convex functions algorithm. Mach learn. : 10...
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 ...
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, ...
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...
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 ...
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...
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...
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-...
Keywords:Criticalpoint,d.c.Functions,Differenceofconvexfunctions,Generalizedderivatives, Minimization,Optimalityconditions,Palais-Smalecondition,Subdifferentials. 1.Introduction Thereisanabundanceofproblemswhichinvolvedifferencefunctions.Wecalla function:onanormedvectorspace(n.v.s.)adifferencefunctionifit ...