نتایج جستجو برای: convex programming

تعداد نتایج: 377543  

Xue-Jie Bai Yan-Kui Liu

Based on credibilistic value-at-risk (CVaR) of regularfuzzy variable, we introduce a new CVaR reduction method fortype-2 fuzzy variables. The reduced fuzzy variables arecharacterized by parametric possibility distributions. We establishsome useful analytical expressions for mean values and secondorder moments of common reduced fuzzy variables. The convex properties of second order moments with ...

Journal: :CoRR 2012
Mikhail Luboschinsky

We propose an Economic Probabilistic analogy: the category of cost is analogous to the category of Probability. The proposed analogy permits construction of an informal theory of nonlinear non-convex Gaussian Utility and Cost, which describes the real economic processes more adequately than a theory based on a linear and convex models. Based on the proposed analogy, we build a nonlinear non-con...

پایان نامه :وزارت علوم، تحقیقات و فناوری - دانشگاه پیام نور استان مازندران - دانشکده ریاضی 1390

abstract this thesis includes five chapter : the first chapter assign to establish fuzzy mathematics requirement and introduction of liner programming in thesis. the second chapter we introduce a multilevel linear programming problems. the third chapter we proposed interactive fuzzy programming which consists of two phases , the study termination conditions of algorithm we show a satisfac...

2016
Matt Wytock Ryan Tibshirani Geoffrey Gordon Arunava Majumdar

Convex optimization has developed a wide variety of useful tools critical to many applications in machine learning. However, unlike linear and quadratic programming, general convex solvers have not yet reached sufficient maturity to fully decouple the convex programming model from the numerical algorithms required for implementation. Especially as datasets grow in size, there is a significant g...

1999
Raphael A. Hauser

The theory of self-scaled conic programming provides a uniied framework for the theories of linear programming, semideenite programming and convex quadratic programming with convex quadratic constraints. The standard search directions for interior-point methods applied to self-scaled conic programming problems are the so-called Nesterov-Todd directions. In this article we show that these direct...

Journal: :Foundations of Computational Mathematics 2015
Dennis Amelunxen Peter Bürgisser

We perform an average analysis of the Grassmann condition number C (A) for the homogeneous convex feasibility problem ∃x ∈ C \ 0 : Ax = 0, where C ⊂ R may be any regular cone. This in particular includes the cases of linear programming, second-order programming, and semidefinite programming. We thus give the first average analysis of convex programming, which is not restricted to linear program...

2006
Klaus Schittkowski Christian Zillober

Abs t rac t We introduce some methods for constrained nonlinear programming that are widely used in practice and that are known under the names SQP for sequential quadratic programming and SCP for sequential convex programming. In both cases, convex subproblems are formulated, in the first case a quadratic programming problem, in the second case a separable nonlinear program in inverse variable...

Journal: :Filomat 2022

Generalized algebraic operations introduced by Ben-Tal [5] are used to define new classes of generalized convex functions, namely (h,?)?(b,F,?) -convex functions and (h,?)?(b,F,?)-convex in the vectorial case. Further, optimality duality results proved for considered (h,?)- nondifferentiable multiobjective programming problem under assumptions that involved (generalized) (b,F,?)-convex.

2003
Klaus Schittkowski Christian Zillober

We introduce some methods for constrained nonlinear programming that are widely used in practice and that are known under the names SQP for sequential quadratic programming and SCP for sequential convex programming. In both cases, convex subproblems are formulated, in the first case a quadratic programming problem, in the second case a separable nonlinear program in inverse variables. The metho...

2008
CHRISTIAN JANSSON

This survey contains recent developments for computing verified results of convex constrained optimization problems, with emphasis on applications. Especially, we consider the computation of verified error bounds for non-smooth convex conic optimization in the framework of functional analysis, for linear programming, and for semidefinite programming. A discussion of important problem transforma...

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