Search results for "Regular"

showing 10 items of 855 documents

A note on the Bregmanized Total Variation and dual forms

2009

This paper considers two approaches to perform image restoration while preserving the contrast. The first one is the Total Variation-based Bregman iterations while the second consists in the minimization of an energy that involves robust edge preserving regularization. We show that these two approaches can be derived form a common framework. This allows us to deduce new properties and to extend and generalize these two previous approaches.

Mathematical optimizationNoise measurementIterative methodCommon frameworkMinificationTotal variation denoisingAlgorithmRegularization (mathematics)Image restorationMathematics2009 16th IEEE International Conference on Image Processing (ICIP)
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Fixed domain approaches in shape optimization problems

2012

This work is a review of results in the approximation of optimal design problems, defined in variable/unknown domains, based on associated optimization problems defined in a fixed ?hold-all? domain, including the family of all admissible open sets. The literature in this respect is very rich and we concentrate on three main approaches: penalization?regularization, finite element discretization on a fixed grid, controllability and control properties of elliptic systems. Comparison with other fixed domain approaches or, in general, with other methods in shape optimization is performed as well and several numerical examples are included.

Mathematical optimizationOptimization problemDiscretizationApplied MathematicsOpen setGridRegularization (mathematics)Finite element methodComputer Science ApplicationsTheoretical Computer ScienceControllabilitySignal ProcessingShape optimizationMathematical PhysicsMathematicsInverse Problems
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Least-Norm Regularization For Weak Two-Level Optimization Problems

1992

In this paper, we consider a regularization for weak two-level optimization problems by adaptation of the method presented by Solohovic (1970). Existence and approximation results are given in the case in which the constraints to the lower level problems are described by a multifunction. Convergence results for the least-norm regularization under perturbations are also presented.

Mathematical optimizationOptimization problemNorm (mathematics)Proximal gradient methods for learningRegularization perspectives on support vector machinesBackus–Gilbert methodRegularization (mathematics)Mathematics
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A genetic algorithm for discrete tomography reconstruction

2007

The aim of this paper is the description of an experiment carried out to verify the robustness of two different approaches for the reconstruction of convex polyominoes in discrete tomography. This is a new field of research, because it differs from classic computerized tomography, and several problems are still open. In particular, the stability problem is tackled by using both a modified version of a known algorithm and a new genetic approach. The effect of both, instrumental and quantization noises has been considered too. © 2007 Springer Science+Business Media, LLC.

Mathematical optimizationPolyominoComputer scienceQuantization (signal processing)Physics::Medical PhysicsComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONRegular polygonDiscrete tomographyStability problemComputer Science ApplicationsTheoretical Computer ScienceGenetic algorithmArtificial IntelligenceHardware and ArchitectureTomographyAlgorithmDiscrete tomographySoftwareGenetic Programming and Evolvable Machines
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An experimental study of the stability problem in discrete tomography

2003

This paper introduces the topic of discrete tomography, briefly showing its main applications, algorithms and new prospects of research. It focuses on the still open problem of stability, facing it from an experimental point of view. In particular an extensive simulation lets verify the robustness of a well known reconstruction technique for binary convex objects, calculating the probability of finding solutions compatible with a given set of noisy projections. © 2005 Elsevier Ltd. All rights reserved.

Mathematical optimizationSettore INF/01 - InformaticaOpen problemApplied MathematicsRegular polygonBinary numberConvex reconstructionDiscrete tomographyStability problemRobustness (computer science)Discrete Mathematics and CombinatoricsDiscrete tomographyDiscrete Mathematics and CombinatoricMathematics
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On the checking of g-coherence of conditional probability bounds

2003

We illustrate an approach to uncertain knowledge based on lower conditional probability bounds. We exploit the coherence principle of de Finetti and a related notion of generalized coherence (g-coherence), which is equivalent to the "avoiding uniform loss" property introduced by Walley for lower and upper probabilities. Based on the additive structure of random gains, we define suitable notions of non relevant gains and of basic sets of variables. Exploiting them, the linear systems in our algorithms can work with reduced sets of variables and/or constraints. In this paper, we illustrate the notions of non relevant gain and of basic set by examining several cases of imprecise assessments d…

Mathematical optimizationSettore MAT/06 - Probabilita' E Statistica MatematicaPosterior probabilityConditional probability tablealgorithmslower conditional probability boundRegular conditional probabilityalgorithms; generalized coherence; linear systems; lower conditional probability bounds; probabilistic reasoning; reduced sets of variables and constraints.Artificial Intelligencelinear systemprobabilistic reasoninggeneralized coherenceMathematicsDiscrete mathematicsreduced sets of variables and constraintsalgorithmlinear systemsProbabilistic logicLaw of total probabilityConditional probabilityCoherence (philosophical gambling strategy)Conditional probability distributionControl and Systems Engineeringlower conditional probability boundsSoftwareInformation Systems
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Solving the Discrete Multiple Criteria Problem using Convex Cones

1984

An interactive method employing pairwise comparisons of attainable solutions is developed for solving the discrete, deterministic multiple criteria problem assuming a single decision maker who has an implicit quasi-concave increasing utility (or value) function. The method chooses an arbitrary set of positive multipliers to generate a proxy composite linear objective function which is then maximized over the set of solutions. The maximizing solution is compared with several solutions using pairwise judgments asked of the decision maker. Responses are used to eliminate alternatives using convex cones based on expressed preferences, and then a new set of weights is found that satisfies the i…

Mathematical optimizationStrategy and ManagementRegular polygonMultiple criteriaPairwise comparisonManagement Science and Operations ResearchDecision makerProxy (statistics)Mathematical proofMathematicsDecision analysismultiattribute programming: multiple criteria convex cones [decision analysis utility/preference]Management Science
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Robust and Efficient IMEX Schemes for Option Pricing under Jump-Diffusion Models

2013

We propose families of IMEX time discretization schemes for the partial integro-differential equation derived for the pricing of options under a jump diffusion process. The schemes include the families of IMEX-midpoint, IMEXCNAB and IMEX-BDF2 schemes. Each family is defined by a convex parameter c ∈ [0, 1], which divides the zeroth-order term due to the jumps between the implicit and explicit part in the time discretization. These IMEX schemes lead to tridiagonal systems, which can be solved extremely efficiently. The schemes are studied through Fourier stability analysis and numerical experiments. It is found that, under suitable assumptions and time step restrictions, the IMEX-midpoint fa…

Mathematical optimizationTridiagonal matrixDiscretizationJump diffusionRegular polygonComputer Science::Numerical AnalysisStability (probability)Mathematics::Numerical Analysissymbols.namesakeFourier transformValuation of optionssymbolsMathematicsLinear multistep methodSSRN Electronic Journal
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Atmospheric Turbulence Effects Removal on Infrared Sequences Degraded by Local Isoplanatism

2007

When observing an object horizontally at a long distance, degradations due to atmospheric turbulence often occur. Different methods have already been tested to get rid of this kind of degradation, especially on infrared sequences. It has been shown that the Wiener filter applied locally on each frame of a sequence allows to obtain good results in terms of edges, while the regularization by the Laplacian operator applied in the same way provides good results in terms of noise removal in uniform areas. In this article, we present hybrid methods which take advantages of both Wiener filter and Laplacian regularization.

Mathematical optimizationsymbols.namesakeSequenceInfraredFrame (networking)Wiener filtersymbolsAtmospheric turbulenceRegularization (mathematics)Laplace operatorAlgorithmMathematicsDegradation (telecommunications)
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Quasiregular ellipticity of open and generalized manifolds

2014

We study the existence of geometrically controlled branched covering maps from \(\mathbb R^3\) to open \(3\)-manifolds or to decomposition spaces \(\mathbb {S}^3/G\), and from \(\mathbb {S}^3/G\) to \(\mathbb {S}^3\).

Mathematics - Complex VariablesApplied Mathematics010102 general mathematicsquasiregular mappingsdecomposition spacesGeometric Topology (math.GT)Metric Geometry (math.MG)01 natural sciencesCombinatoricsMathematics - Geometric Topologysemmes metricsComputational Theory and MathematicsMathematics - Metric Geometryquasiregular ellipticity0103 physical sciencesFOS: Mathematics30C65 (Primary) 30L10 (Secondary)010307 mathematical physicsBranched covering0101 mathematicsComplex Variables (math.CV)AnalysisMathematics
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