On a theorem due to Crouzeix and Ferland
Nonsmooth analysis, Nonsmooth optimization, Generalized convexity, KT pseudoconvex problems, FJ pseudoconvex problems, Quasiconvex programming, 90C26, 26B25, 49J52,
Vsevolod Ivanov
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A new optimization method based on Perry's idea through the use of the matrix power [PDF]
, 2021 MSC 2010: 90C30, 90C26, 90C06The purpose of this paper is to present a new conjugate gradient method for solving unconstrained nonlinear optimization problems, based on Perry’s idea.
Imane Hafaidia, Noureddine Benrabia, Mourad Ghiat, Hamza Guebbai
core
Two level hierarchical time minimizing transportation problem
Global optimization, concave minimization problem, time minimizing transportation problem, hierarchical optimization, 90C27, 90C26, 90C08, 90C90,
Sonia, Munish Puri
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Performance Bounds For Co-/Sparse Box Constrained Signal Recovery
Analele Stiintifice ale Universitatii Ovidius Constanta: Seria Matematica, 2019 The recovery of structured signals from a few linear measurements is a central point in both compressed sensing (CS) and discrete tomography. In CS the signal structure is described by means of a low complexity model e.g. co-/sparsity.
Kuske Jan, Petra Stefania
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About the non-convex optimization problem induced by non-positive semidefinite kernel learning
Support vector machine, Kernel methods, Non-convex optimization, 90C26, 49Q, 74P05, C45, C14,
Katharina Morik, Ingo Mierswa
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New Hybrid Conjugate Gradient Method as a Convex Combination of PRP and RMIL+ Methods [PDF]
The Conjugate Gradient (CG) method is a powerful iterative approach for solving large-scale minimization problems, characterized by its simplicity, low computation cost and good convergence. In this paper, a new hybrid conjugate gradient HLB method (HLB:
BENZINE, Rachid +3 more
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A new hybrid conjugate gradient algorithm based on the Newton direction to solve unconstrained optimization problems [PDF]
, 2023 https://doi.org/10.1007/s12190-022-01821-zIn this paper, we propose a new hybrid conjugate gradient method to solve unconstrained optimization problems. This new method is defined as a convex combination of DY and DL conjugate gradient methods.
Naima Hamel, Noureddine Benrabia, Mourad Ghiat, Hamza Guebbai
core
Duality and optimality conditions for generalized equilibrium problems involving DC functions
Equilibrium problems, Duality, Fenchel conjugation, DC functions, Variational inequalities, 49N15, 58E35, 90C26, 90C46,
N. Dinh, J. Strodiot, V. Nguyen
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Geometric approaches to matrix normalization and graph balancing
Forum of Mathematics, SigmaNormal matrices, or matrices which commute with their adjoints, are of fundamental importance in pure and applied mathematics. In this paper, we study a natural functional on the space of square complex matrices whose global minimizers are normal ...
Tom Needham, Clayton Shonkwiler
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A primal dual modified subgradient algorithm with sharp Lagrangian
Nonsmooth optimization, Nonconvex optimization, Duality scheme, Sharp Lagrangian, Modified subgradient algorithm, 90C26, 49M29, 49M37,
Alfredo Iusem +2 more
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