Results 41 to 50 of about 182 (91)
GLOBAL OPTIMIZATION APPROACH TO UTILITY MAXIMIZATION PROBLEM
, 2015 We consider the utility maximization problem for oligopsonistic market which is nonconvex optimization problem. Unlike the utility maximization for competitive market, the problem belongs to a class of global optimization. The purpose of this paper is to
R. Enkhbat, J. Enkhbayar, A. Griewank
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A hybrid approach to the solution of a pricing model with continuous demand segmentation
EURO Journal on Computational Optimization, 2013 Price optimization fits naturally the framework of bilevel programming, where a leader integrates within its decision process the reaction of rational customers.
Patrice Marcotte +2 more
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A shrinkage-thresholding projection method for sparsest solutions of LCPs
, 2014 In this paper, we study the sparsest solutions of linear complementarity problems (LCPs), which study has many applications, such as bimatrix games and portfolio selections. Mathematically, the underlying model is NP-hard in general.
Meijuan Shang, C. Nie
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Relaxed η-α quasimonotone and application to the generalized variational-like inequality problem
, 2013 In this paper, some new mappings called relaxed η-α quasimonotone and a relaxed η-α properly quasimonotone operator are first introduced. The relationships between them are obtained.
Qiao-fang Chen, Jie Luo
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Nonsmooth spectral gradient methods for unconstrained optimization
EURO Journal on Computational Optimization, 2017 To solve nonsmooth unconstrained minimization problems, we combine the spectral choice of step length with two well-established subdifferential-type schemes: the gradient sampling method and the simplex gradient method.
Milagros Loreto +3 more
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On global optimization with indefinite quadratics
EURO Journal on Computational Optimization, 2017 We present an algorithmic framework for global optimization problems in which the non-convexity is manifested as an indefinite-quadratic as part of the objective function.
Marcia Fampa, Jon Lee, Wendel Melo
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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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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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Journal of Inequalities and Applications, 2011 In this paper, an efficient modified nonlinear conjugate gradient method for solving unconstrained optimization problems is proposed. An attractive property of the modified method is that the generated direction in each step is always descending without ...
Liu Jinkui, Wang Shaoheng
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Consensus-based optimisation with truncated noise
European Journal of Applied MathematicsConsensus-based optimisation (CBO) is a versatile multi-particle metaheuristic optimisation method suitable for performing non-convex and non-smooth global optimisations in high dimensions.
Massimo Fornasier +3 more
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