06416nam 2200577 450 99646640760331620230619190008.03-030-83442-5(CKB)5470000001298869(MiAaPQ)EBC6796401(Au-PeEL)EBL6796401(OCoLC)1281772391(PPN)258298448(EXLCZ)99547000000129886920220721d2021 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierMathematical control and numerical applications JANO13, Khouribga, Morocco, February 22-24, 2021 /Abdeljalil Nachaoui, Abdelilah Hakim, Amine Laghrib, editorsCham, Switzerland :Springer,[2021]©20211 online resource (168 pages)Springer Proceedings in Mathematics & Statistics ;Volume 3723-030-83441-7 Intro -- Preface -- Contents -- VRP with Flexible Time Windows Using the ALNS Metaheuristic Algorithm -- 1 Problem Formulation of the VRPFlexTW -- 2 ALNS State of the Art in the Context of VRP -- 3 ALNS Techniques for VRPFlexTW -- 4 Numerical Experiment -- 5 Conclusion -- References -- VRPTW: From Mathematical Models to Computing Science Tools -- 1 Introduction -- 2 Mathematical Formulation -- 3 Adaptive Large Neighborhood Search -- 4 Deterministic Vehicle Routing Problem with Time Windows (VRPTW) -- 4.1 The Modified Adaptive Large Neighborhood Search (MALNS) -- 4.2 The Multi-threading Parallel ALNS -- 5 Robust Vehicle Routing Problem with Time Windows (RVRPTW) -- 5.1 Sequential Approach to Solve the RVRPTW -- 5.2 Multi-threading Parallel Approach to Solve the RVRPTW -- 6 Computational Experiments -- 6.1 Computational Results of the MALNS -- 6.2 Computational Results of the Multi-threading Parallel ALNS -- 6.3 Computational Results of the Sequential Robust Approach -- 6.4 Computational Results of the Parallel Robust Approaches -- 7 Conclusion -- References -- An Anisotropic PDE for Multi-frame Super-Resolution Image Reconstruction -- 1 Multi-frame SR Model -- 2 Discritization and Numerical Result -- References -- Acceleration of the KMF Algorithm Convergence to Solve the Cauchy Problem for Poisson's Equation -- 1 Introduction -- 2 Cauchy Problem for Poisson's Equation and JN Relaxed Algorithm -- 2.1 The JN Relaxed Algorithm -- 3 Convergence Analysis -- 4 Acceleration of Convergence -- 5 Numerical Results -- 6 Conclusion -- References -- Identification of Genuine from Fake Banknotes Using an Enhanced Machine Learning Approach -- 1 Introduction -- 2 Formulating the Binary Classification Problem -- 2.1 Kernel Function -- 3 Hinge Loss Function -- 4 Experimental Results -- 4.1 Feature Importance -- 5 Conclusion -- References.Identification of Robin Coefficient in Elliptic Problem by a Coupled Complex Boundary Method -- 1 Introduction -- 2 A Reformulation with CCBM -- 3 Tikhonov Regularization -- 4 Numerical Results -- 4.1 Identification Results Without Noise -- 4.2 Identification Results with Noise -- References -- A New Space-Variant Optimization Approach for Image Segmentation -- 1 Introduction -- 2 The Problem Formulation -- 3 The ADMM Algorithm -- 4 Numerical Results -- 5 Conclusion -- References -- A Lagrangian Mixed Finite Elements Method for Advection-Diffusion Equations -- 1 Introduction -- 2 Eulerian-Lagrangian Method -- 2.1 Lagrangian Discretization of the Advection Problem -- 2.2 Eulerian Discretization of the Diffusion Subproblem -- 2.3 Particle-Mesh Interaction -- 2.4 Mesh-Particle Projection -- 3 Numerical Experiments -- References -- Adaptation by Nash Games in Gradient-Based Multi-objective/Multi-disciplinary Optimization -- 1 Introduction -- 2 Nash-Game-Based Adaptation Algorithm -- 2.1 Mathematical Problem Definition -- 2.2 Convex Hull, Common Descent Direction and Directional Derivatives -- 2.3 Preliminary Calculations -- 2.4 Nash Game Formulation and Statement of Theoretical Result -- 3 Proof and Convergence Rate -- 3.1 Compatibility -- 3.2 Continuum of Nash Equilibria -- 3.3 Variation of the Primary Cost Functions Along the Continuum -- 3.4 Variation of the Secondary Cost Functions Along the Continuum -- 3.5 Secondary Cost Functions -- 4 Illustration of the Prioritized Multi-objective Optimization Approach -- 4.1 Numerical Implementation -- 4.2 Application to the Sizing of a Sandwich Element for Optimal Mechanical Resistance -- 5 Conclusion and Perspective -- References -- Blind Noisy Mixture Separation for Dependent Sources -- 1 Introduction -- 2 Problem Statement -- 3 First Step: Denoising of Observations -- 3.1 Regularization Total Variation.3.2 Discretization -- 3.3 Algorithme de Projection de Chambolle -- 4 Second Step: BSS Based on the Combination of Copula and TV -- 4.1 Case of Independent Sources -- 4.2 Case of Dependent Sources -- 5 Simulation Results -- 6 Conclusion -- References -- Blind Separation of Dependent Sources Using Copula -- 1 Introduction -- 2 Problem Statement -- 3 Copula -- 4 Case of Independent Sources -- 5 Case of Dependent Sources -- 5.1 Procedure 1: Dependent BSS Based on a Priori Knowledge on the Sources -- 5.2 Procedure 2: BSS for Dependent Sources When the Dependency Model Is Known and the Parameter Is Unknown -- 5.3 Procedure 3: BSS for Dependent Sources When the Dependency Model Is Unknown and the Parameter Is Unknown -- 6 Experimental Performance Study -- 6.1 Series 1: Tests with Various Types of Signals -- 6.2 Series 2: Tests with Images -- 7 Conclusion -- References.Springer proceedings in mathematics & statistics ;Volume 372.Numerical analysisCongressesComputer scienceMathematicsCongressesAnàlisi numèricathubInformàticathubCongressosthubLlibres electrònicsthubNumerical analysisComputer scienceMathematicsAnàlisi numèricaInformàtica519.4Nachaoui AbdeljalilHakim AbdelilahLaghrib AmineMiAaPQMiAaPQMiAaPQBOOK996466407603316Mathematical control and numerical applications2901142UNISA