A reduced formulation for pseudoinvex vector functions
نویسندگان
چکیده
منابع مشابه
An Explicit Formulation for Two Dimensional Vector Partition Functions
In this paper, an explicit formulation for two dimensional multivariate truncated power functions is presented, and a simplified explicit formulation for two dimensional vector partition functions is given. Moreover, Popoviciu’s formulation for restricted integer partition functions is generalized and the generalized Frobenius problem is also discussed.
متن کاملSemi E-pseudoinvex and Semi E-quasiinvex Functions and Applications
In this paper, we introduce three new types of generalized convex functions, called semi E-pseudoinvex, strictly semi Epseudoinvex and semi E-quasiinvex functions. Then some of their basic properties are studied, We used directional derivative and obtain the new results in this class of functions. As applications of our results, we obtain optimal solutions for multiobjective programming. AMS Su...
متن کاملA New Formulation for Cost-Sensitive Two Group Support Vector Machine with Multiple Error Rate
Support vector machine (SVM) is a popular classification technique which classifies data using a max-margin separator hyperplane. The normal vector and bias of the mentioned hyperplane is determined by solving a quadratic model implies that SVM training confronts by an optimization problem. Among of the extensions of SVM, cost-sensitive scheme refers to a model with multiple costs which conside...
متن کاملA bilinear formulation for vector sparsity optimization
Sparsity plays an important role in many fields of engineering. The cardinality penalty function, often used as a measure of sparsity, is neither continuous nor differentiable and therefore smooth optimization algorithms cannot be applied directly. In this paper we present a continuous yet non-differentiable sparsity function which constitutes a tight lower bound on the cardinality function. Th...
متن کاملA General Formulation for Support Vector Machines
In this paper, we derive a general formulation of support vector machines for classification and regression respectively. Le loss function is proposed as a patch of L1 and L2 soft margin loss functions for classifier, while soft insensitive loss function is introduced as the generalization of popular loss functions for regression. The introduction of the two loss functions results in a general ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Annals of Operations Research
سال: 2016
ISSN: 0254-5330,1572-9338
DOI: 10.1007/s10479-016-2372-4