Results 1 to 10 of about 54 (54)
On Shor's r-Algorithm for Problems with Constraints
Кібернетика та комп'ютерні технології, 2023 Introduction. Nonsmooth optimization problems arise in a wide range of applications, including engineering, finance, and deep learning, where activation functions often have discontinuous derivatives, such as ReLU.
Vladimir Norkin, Anton Kozyriev
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IEEE Access, 2022 Quaternions have appeared in many practical fields, such as image processing and data mining, and so on. This paper focuses on designing an efficient quaternion-valued neurodynamic approach (QNA) based on multi-agent systems to solve nonsmooth convex ...
Guocheng Li +3 more
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Use of the Shor’s r-Algorithm in Linear Robust Optimization Problems
Кібернетика та комп'ютерні технології, 2021 The paper is devoted to the description of a new approach to the construction of algorithms for solving linear programming problems (LP-problems), in which the number of constraints is much greater than the number of variables.
P. Stetsyuk +3 more
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GNU Octave and Python Implementation of Shor's r-Algorithm with Adaptive Step Control
Кібернетика та комп'ютерні технології, 2022 r-algorithms, or subgradient methods with dilation of space in the direction of the difference of two sequential subgradients, were proposed by N.Z.Shor in 1970 in his doctoral thesis. Respective software implementations proved to be competitive with the
Petro Stetsyuk +2 more
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A nonmonotone line search method for solving constrained multiobjective optimization problems [PDF]
Iranian Journal of Numerical Analysis and OptimizationIn this paper, we propose a globally convergent Sequential Quadratic Programming (SQP) method for solving constrained multiobjective optimization problems (MOPs) with inequality constraints.
Mehri Rashidi, Esmaile Khorram
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A family of global convergent inexact secant methods for nonconvex constrained optimization
Journal of Algorithms & Computational Technology, 2018 We present a family of new inexact secant methods in association with Armijo line search technique for solving nonconvex constrained optimization. Different from the existing inexact secant methods, the algorithms proposed in this paper need not compute ...
Zhujun Wang, Li Cai, Zheng Peng
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A UV-Method for a Class of Constrained Minimized Problems of Maximum Eigenvalue Functions
Journal of Function Spaces, 2017 In this paper, we apply the UV-algorithm to solve the constrained minimization problem of a maximum eigenvalue function which is the composite function of an affine matrix-valued mapping and its maximum eigenvalue.
Wei Wang +3 more
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Nonconvex Penalized Regularization for Robust Sparse Recovery in the Presence of
IEEE Access, 2018 Nonconvex penalties have recently received considerable attention in sparse recovery based on Gaussian assumptions. However, many sparse recovery problems occur in the presence of impulsive noises. This paper is concerned with the analysis and comparison
Yunyi Li +5 more
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Sparse Learning of the Disease Severity Score for High-Dimensional Data
Complexity, 2017 Learning disease severity scores automatically from collected measurements may aid in the quality of both healthcare and scientific understanding.
Ivan Stojkovic, Zoran Obradovic
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Journal of MathematicsThis paper defines a strong convertible nonconvex (SCN) function for solving the unconstrained optimization problems with the nonconvex or nonsmooth (nondifferentiable) function.
Min Jiang +3 more
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