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Modeling, simulation and optimization in the health- and energy-sector / / René Pinnau, Nicolas R. Gauger, Axel Klar, editors
| Modeling, simulation and optimization in the health- and energy-sector / / René Pinnau, Nicolas R. Gauger, Axel Klar, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (153 pages) |
| Disciplina | 511.8 |
| Collana | SEMA SIMAI Springer series |
| Soggetto topico |
Mathematical models
Mathematical optimization Models matemàtics Optimització matemàtica |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-030-99983-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996479367703316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Modeling, simulation and optimization in the health- and energy-sector / / René Pinnau, Nicolas R. Gauger, Axel Klar, editors
| Modeling, simulation and optimization in the health- and energy-sector / / René Pinnau, Nicolas R. Gauger, Axel Klar, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (153 pages) |
| Disciplina | 511.8 |
| Collana | SEMA SIMAI Springer series |
| Soggetto topico |
Mathematical models
Mathematical optimization Models matemàtics Optimització matemàtica |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-030-99983-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910574856003321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Modeling, Simulation and Optimization of Fluid Dynamic Applications [[electronic resource] /] / edited by Armin Iske, Thomas Rung
| Modeling, Simulation and Optimization of Fluid Dynamic Applications [[electronic resource] /] / edited by Armin Iske, Thomas Rung |
| Autore | Iske Armin |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (165 pages) |
| Disciplina | 003.3 |
| Altri autori (Persone) | RungThomas |
| Collana | Lecture Notes in Computational Science and Engineering |
| Soggetto topico |
Mathematics - Data processing
Computational Science and Engineering Computational Mathematics and Numerical Analysis Optimització matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-45158-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Lower Bounds for the Advection-Hyperdiffusion Equation -- 2. Modeling and Simulation of Parabolic Trough Collectors using Nanofluids -- 3. Adaptive DG Methods for 1D unsteady Convection-Diffusion Problems on a Moving Mesh -- 4. Anisotropic Kernels for Particle Flow Simulation -- 5. An Error-Based Low-Rank Correction for Pressure Schur Complement Preconditioners -- 6. Radon-based Image Reconstruction for MPI using a continuously rotating FFL -- 7. Numerical Simulation of an idealized coupled Ocean-Atmosphere Climate Model -- 8. Application of p-Laplacian relaxed steepest Descent to Shape Optimizations in two-phase Flows -- 9. Computing High-Order p-Harmonic Descent Directions and Their Limits in Shape Optimization. |
| Record Nr. | UNINA-9910770279203321 |
Iske Armin
|
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Modern numerical nonlinear optimization / / Neculai Andrei
| Modern numerical nonlinear optimization / / Neculai Andrei |
| Autore | Andrei Neculai |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (824 pages) |
| Disciplina | 016.5192 |
| Collana | Springer Optimization and Its Applications |
| Soggetto topico |
Mathematical optimization
Algebras, Linear Optimització matemàtica Àlgebra lineal |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783031087202
9783031087196 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- List of Algorithms -- List of Applications -- List of Figures -- List of Tables -- 1: Introduction -- 1.1 Mathematical Modeling: Linguistic Models Versus Mathematical Models -- 1.2 Mathematical Modeling and Computational Sciences -- 1.3 The Modern Modeling Scheme for Optimization -- 1.4 Classification of Optimization Problems -- 1.5 Optimization Algorithms -- 1.6 Collections of Applications for Numerical Experiments -- 1.7 Comparison of Algorithms -- 1.8 The Structure of the Book -- 2: Fundamentals on Unconstrained Optimization. Stepsize Computation -- 2.1 The Problem -- 2.2 Fundamentals on the Convergence of the Line-Search Methods -- 2.3 The General Algorithm for Unconstrained Optimization -- 2.4 Convergence of the Algorithm with Exact Line-Search -- 2.5 Inexact Line-Search Methods -- 2.6 Convergence of the Algorithm with Inexact Line-Search -- 2.7 Three Fortran Implementations of the Inexact Line-Search -- 2.8 Numerical Studies: Stepsize Computation -- 3: Steepest Descent Methods -- 3.1 The Steepest Descent -- Convergence of the Steepest Descent Method for Quadratic Functions -- Inequality of Kantorovich -- Numerical Study -- Convergence of the Steepest Descent Method for General Functions -- 3.2 The Relaxed Steepest Descent -- Numerical Study: SDB Versus RSDB -- 3.3 The Accelerated Steepest Descent -- Numerical Study -- 3.4 Comments on the Acceleration Scheme -- 4: The Newton Method -- 4.1 The Newton Method for Solving Nonlinear Algebraic Systems -- 4.2 The Gauss-Newton Method -- 4.3 The Newton Method for Function Minimization -- 4.4 The Newton Method with Line-Search -- 4.5 Analysis of Complexity -- 4.6 The Modified Newton Method -- 4.7 The Newton Method with Finite-Differences -- 4.8 Errors in Functions, Gradients, and Hessians -- 4.9 Negative Curvature Direction Methods -- 4.10 The Composite Newton Method.
5: Conjugate Gradient Methods -- 5.1 The Concept of Nonlinear Conjugate Gradient -- 5.2 The Linear Conjugate Gradient Method -- The Linear Conjugate Gradient Algorithm -- Convergence Rate of the Linear Conjugate Gradient Algorithm -- Preconditioning -- Incomplete Cholesky Factorization -- Comparison of the Convergence Rate of the Linear Conjugate Gradient and of the Steepest Descent -- 5.3 General Convergence Results for Nonlinear Conjugate Gradient Methods -- Convergence Under the Strong Wolfe Line-Search -- Convergence Under the Wolfe Line-Search -- 5.4 Standard Conjugate Gradient Methods -- Conjugate Gradient Methods with gk+12 in the Numerator of βk -- The Fletcher-Reeves Method -- The CD Method -- The Dai-Yuan Method -- Conjugate Gradient Methods with in the Numerator of βk -- The Polak-Ribière-Polyak Method -- The Hestenes-Stiefel Method -- The Liu-Storey Method -- Numerical Study: Standard Conjugate Gradient Methods -- 5.5 Hybrid Conjugate Gradient Methods -- Hybrid Conjugate Gradient Methods Based on the Projection Concept -- Numerical Study: Hybrid Conjugate Gradient Methods -- Hybrid Conjugate Gradient Methods as Convex Combinations of the Standard Conjugate Gradient Methods -- The Hybrid Convex Combination of LS and DY -- Numerical Study: NDLSDY -- 5.6 Conjugate Gradient Methods as Modifications of the Standard Schemes -- The Dai-Liao Conjugate Gradient Method -- The Conjugate Gradient with Guaranteed Descent (CG-DESCENT) -- Numerical Study: CG-DESCENT -- The Conjugate Gradient with Guaranteed Descent and Conjugacy Conditions and a Modified Wolfe Line-Search (DESCON) -- Numerical Study: DESCON -- 5.7 Conjugate Gradient Methods Memoryless BFGS Preconditioned -- The Memoryless BFGS Preconditioned Conjugate Gradient (CONMIN) -- Numerical Study: CONMIN. The Conjugate Gradient Method Closest to the Scaled Memoryless BFGS Search Direction (DK / CGOPT) -- Numerical Study: DK/CGOPT -- 5.8 Solving Large-Scale Applications -- 6: Quasi-Newton Methods -- 6.1 DFP and BFGS Methods -- 6.2 Modifications of the BFGS Method -- 6.3 Quasi-Newton Methods with Diagonal Updating of the Hessian -- 6.4 Limited-Memory Quasi-Newton Methods -- 6.5 The SR1 Method -- 6.6 Sparse Quasi-Newton Updates -- 6.7 Quasi-Newton Methods and Separable Functions -- 6.8 Solving Large-Scale Applications -- 7: Inexact Newton Methods -- 7.1 The Inexact Newton Method for Nonlinear Algebraic Systems -- 7.2 Inexact Newton Methods for Functions Minimization -- 7.3 The Line-Search Newton-CG Method -- 7.4 Comparison of TN Versus Conjugate Gradient Algorithms -- 7.5 Comparison of TN Versus L-BFGS -- 7.6 Solving Large-Scale Applications -- 8: The Trust-Region Method -- 8.1 The Trust-Region -- 8.2 Algorithms Based on the Cauchy Point -- 8.3 The Trust-Region Newton-CG Method -- 8.4 The Global Convergence -- 8.5 Iterative Solution of the Subproblem -- 8.6 The Scaled Trust-Region -- 9: Direct Methods for Unconstrained Optimization -- 9.1 The NELMED Algorithm -- 9.2 The NEWUOA Algorithm -- 9.3 The DEEPS Algorithm -- 9.4 Numerical Study: NELMED, NEWUOA, and DEEPS -- 10: Constrained Nonlinear Optimization Methods: An Overview -- 10.1 Convergence Tests -- 10.2 Infeasible Points -- 10.3 Approximate Subproblem: Local Models and Their Solving -- 10.4 Globalization Strategy: Convergence from Remote Starting Points -- 10.5 The Refining the Local Model -- 11: Optimality Conditions for Nonlinear Optimization -- 11.1 General Concepts in Nonlinear Optimization -- 11.2 Optimality Conditions for Unconstrained Optimization -- 11.3 Optimality Conditions for Problems with Inequality Constraints -- 11.4 Optimality Conditions for Problems with Equality Constraints. 11.5 Optimality Conditions for General Nonlinear Optimization Problems -- 11.6 Duality -- 12: Simple Bound Constrained Optimization -- 12.1 Necessary Conditions for Optimality -- 12.2 Sufficient Conditions for Optimality -- 12.3 Methods for Solving Simple Bound Optimization Problems -- 12.4 The Spectral Projected Gradient Method (SPG) -- Numerical Study-SPG: Quadratic Interpolation versus Cubic Interpolation -- 12.5 L-BFGS with Simple Bounds (L-BFGS-B) -- Numerical Study: L-BFGS-B Versus SPG -- 12.6 Truncated Newton with Simple Bounds (TNBC) -- 12.7 Applications -- Application A1 (Elastic-Plastic Torsion) -- Application A2 (Pressure Distribution in a Journal Bearing) -- Application A3 (Optimal Design with Composite Materials) -- Application A4 (Steady-State Combustion) -- Application A6 (Inhomogeneous Superconductors: 1-D Ginzburg-Landau) -- 13: Quadratic Programming -- 13.1 Equality Constrained Quadratic Programming -- Factorization of the Full KKT System -- The Schur-Complement Method -- The Null-Space Method -- Large-Scale Problems -- The Conjugate Gradient Applied to the Reduced System -- The Projected Conjugate Gradient Method -- 13.2 Inequality Constrained Quadratic Programming -- The Primal Active-Set Method -- An Algorithm for Positive Definite Hessian -- Reduced Gradient for Inequality Constraints -- The Reduced Gradient for Simple Bounds -- The Primal-Dual Active-Set Method -- 13.3 Interior Point Methods -- Stepsize Selection -- 13.4 Methods for Convex QP Problems with Equality Constraints -- 13.5 Quadratic Programming with Simple Bounds: The Gradient Projection Method -- The Cauchy Point -- Subproblem Minimization -- 13.6 Elimination of Variables -- 14: Penalty and Augmented Lagrangian Methods -- 14.1 The Quadratic Penalty Method -- 14.2 The Nonsmooth Penalty Method -- 14.3 The Augmented Lagrangian Method. 14.4 Criticism of the Penalty and Augmented Lagrangian Methods -- 14.5 A Penalty-Barrier Algorithm (SPENBAR) -- The Penalty-Barrier Method -- Global Convergence -- Numerical Study-SPENBAR: Solving Applications from the LACOP Collection -- 14.6 The Linearly Constrained Augmented Lagrangian (MINOS) -- MINOS for Linear Constraints -- Numerical Study: MINOS for Linear Programming -- MINOS for Nonlinear Constraints -- Numerical Study-MINOS: Solving Applications from the LACOP Collection -- 15: Sequential Quadratic Programming -- 15.1 A Simple Approach to SQP -- 15.2 Reduced-Hessian Quasi-Newton Approximations -- 15.3 Merit Functions -- 15.4 Second-Order Correction (Maratos Effect) -- 15.5 The Line-Search SQP Algorithm -- 15.6 The Trust-Region SQP Algorithm -- 15.7 Sequential Linear-Quadratic Programming (SLQP) -- 15.8 A SQP Algorithm for Large-Scale-Constrained Optimization (SNOPT) -- 15.9 A SQP Algorithm with Successive Error Restoration (NLPQLP) -- 15.10 Active-Set Sequential Linear-Quadratic Programming (KNITRO/ACTIVE) -- 16: Primal Methods: The Generalized Reduced Gradient with Sequential Linearization -- 16.1 Feasible Direction Methods -- 16.2 Active Set Methods -- 16.3 The Gradient Projection Method -- 16.4 The Reduced Gradient Method -- 16.5 The Convex Simplex Method -- 16.6 The Generalized Reduced Gradient Method (GRG) -- 16.7 GRG with Sequential Linear or Sequential Quadratic Programming (CONOPT) -- 17: Interior-Point Methods -- 17.1 Prototype of the Interior-Point Algorithm -- 17.2 Aspects of the Algorithmic Developments -- 17.3 Line-Search Interior-Point Algorithm -- 17.4 A Variant of the Line-Search Interior-Point Algorithm -- 17.5 Trust-Region Interior-Point Algorithm -- 17.6 Interior-Point Sequential Linear-Quadratic Programming (KNITRO/INTERIOR) -- 18: Filter Methods -- 18.1 Sequential Linear Programming Filter Algorithm. 18.2 Sequential Quadratic Programming Filter Algorithm. |
| Record Nr. | UNINA-9910619281203321 |
|
Andrei Neculai
|
||
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Modern numerical nonlinear optimization / / Neculai Andrei
| Modern numerical nonlinear optimization / / Neculai Andrei |
| Autore | Andrei Neculai |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (824 pages) |
| Disciplina | 016.5192 |
| Collana | Springer Optimization and Its Applications |
| Soggetto topico |
Mathematical optimization
Algebras, Linear Optimització matemàtica Àlgebra lineal |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783031087202
9783031087196 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- List of Algorithms -- List of Applications -- List of Figures -- List of Tables -- 1: Introduction -- 1.1 Mathematical Modeling: Linguistic Models Versus Mathematical Models -- 1.2 Mathematical Modeling and Computational Sciences -- 1.3 The Modern Modeling Scheme for Optimization -- 1.4 Classification of Optimization Problems -- 1.5 Optimization Algorithms -- 1.6 Collections of Applications for Numerical Experiments -- 1.7 Comparison of Algorithms -- 1.8 The Structure of the Book -- 2: Fundamentals on Unconstrained Optimization. Stepsize Computation -- 2.1 The Problem -- 2.2 Fundamentals on the Convergence of the Line-Search Methods -- 2.3 The General Algorithm for Unconstrained Optimization -- 2.4 Convergence of the Algorithm with Exact Line-Search -- 2.5 Inexact Line-Search Methods -- 2.6 Convergence of the Algorithm with Inexact Line-Search -- 2.7 Three Fortran Implementations of the Inexact Line-Search -- 2.8 Numerical Studies: Stepsize Computation -- 3: Steepest Descent Methods -- 3.1 The Steepest Descent -- Convergence of the Steepest Descent Method for Quadratic Functions -- Inequality of Kantorovich -- Numerical Study -- Convergence of the Steepest Descent Method for General Functions -- 3.2 The Relaxed Steepest Descent -- Numerical Study: SDB Versus RSDB -- 3.3 The Accelerated Steepest Descent -- Numerical Study -- 3.4 Comments on the Acceleration Scheme -- 4: The Newton Method -- 4.1 The Newton Method for Solving Nonlinear Algebraic Systems -- 4.2 The Gauss-Newton Method -- 4.3 The Newton Method for Function Minimization -- 4.4 The Newton Method with Line-Search -- 4.5 Analysis of Complexity -- 4.6 The Modified Newton Method -- 4.7 The Newton Method with Finite-Differences -- 4.8 Errors in Functions, Gradients, and Hessians -- 4.9 Negative Curvature Direction Methods -- 4.10 The Composite Newton Method.
5: Conjugate Gradient Methods -- 5.1 The Concept of Nonlinear Conjugate Gradient -- 5.2 The Linear Conjugate Gradient Method -- The Linear Conjugate Gradient Algorithm -- Convergence Rate of the Linear Conjugate Gradient Algorithm -- Preconditioning -- Incomplete Cholesky Factorization -- Comparison of the Convergence Rate of the Linear Conjugate Gradient and of the Steepest Descent -- 5.3 General Convergence Results for Nonlinear Conjugate Gradient Methods -- Convergence Under the Strong Wolfe Line-Search -- Convergence Under the Wolfe Line-Search -- 5.4 Standard Conjugate Gradient Methods -- Conjugate Gradient Methods with gk+12 in the Numerator of βk -- The Fletcher-Reeves Method -- The CD Method -- The Dai-Yuan Method -- Conjugate Gradient Methods with in the Numerator of βk -- The Polak-Ribière-Polyak Method -- The Hestenes-Stiefel Method -- The Liu-Storey Method -- Numerical Study: Standard Conjugate Gradient Methods -- 5.5 Hybrid Conjugate Gradient Methods -- Hybrid Conjugate Gradient Methods Based on the Projection Concept -- Numerical Study: Hybrid Conjugate Gradient Methods -- Hybrid Conjugate Gradient Methods as Convex Combinations of the Standard Conjugate Gradient Methods -- The Hybrid Convex Combination of LS and DY -- Numerical Study: NDLSDY -- 5.6 Conjugate Gradient Methods as Modifications of the Standard Schemes -- The Dai-Liao Conjugate Gradient Method -- The Conjugate Gradient with Guaranteed Descent (CG-DESCENT) -- Numerical Study: CG-DESCENT -- The Conjugate Gradient with Guaranteed Descent and Conjugacy Conditions and a Modified Wolfe Line-Search (DESCON) -- Numerical Study: DESCON -- 5.7 Conjugate Gradient Methods Memoryless BFGS Preconditioned -- The Memoryless BFGS Preconditioned Conjugate Gradient (CONMIN) -- Numerical Study: CONMIN. The Conjugate Gradient Method Closest to the Scaled Memoryless BFGS Search Direction (DK / CGOPT) -- Numerical Study: DK/CGOPT -- 5.8 Solving Large-Scale Applications -- 6: Quasi-Newton Methods -- 6.1 DFP and BFGS Methods -- 6.2 Modifications of the BFGS Method -- 6.3 Quasi-Newton Methods with Diagonal Updating of the Hessian -- 6.4 Limited-Memory Quasi-Newton Methods -- 6.5 The SR1 Method -- 6.6 Sparse Quasi-Newton Updates -- 6.7 Quasi-Newton Methods and Separable Functions -- 6.8 Solving Large-Scale Applications -- 7: Inexact Newton Methods -- 7.1 The Inexact Newton Method for Nonlinear Algebraic Systems -- 7.2 Inexact Newton Methods for Functions Minimization -- 7.3 The Line-Search Newton-CG Method -- 7.4 Comparison of TN Versus Conjugate Gradient Algorithms -- 7.5 Comparison of TN Versus L-BFGS -- 7.6 Solving Large-Scale Applications -- 8: The Trust-Region Method -- 8.1 The Trust-Region -- 8.2 Algorithms Based on the Cauchy Point -- 8.3 The Trust-Region Newton-CG Method -- 8.4 The Global Convergence -- 8.5 Iterative Solution of the Subproblem -- 8.6 The Scaled Trust-Region -- 9: Direct Methods for Unconstrained Optimization -- 9.1 The NELMED Algorithm -- 9.2 The NEWUOA Algorithm -- 9.3 The DEEPS Algorithm -- 9.4 Numerical Study: NELMED, NEWUOA, and DEEPS -- 10: Constrained Nonlinear Optimization Methods: An Overview -- 10.1 Convergence Tests -- 10.2 Infeasible Points -- 10.3 Approximate Subproblem: Local Models and Their Solving -- 10.4 Globalization Strategy: Convergence from Remote Starting Points -- 10.5 The Refining the Local Model -- 11: Optimality Conditions for Nonlinear Optimization -- 11.1 General Concepts in Nonlinear Optimization -- 11.2 Optimality Conditions for Unconstrained Optimization -- 11.3 Optimality Conditions for Problems with Inequality Constraints -- 11.4 Optimality Conditions for Problems with Equality Constraints. 11.5 Optimality Conditions for General Nonlinear Optimization Problems -- 11.6 Duality -- 12: Simple Bound Constrained Optimization -- 12.1 Necessary Conditions for Optimality -- 12.2 Sufficient Conditions for Optimality -- 12.3 Methods for Solving Simple Bound Optimization Problems -- 12.4 The Spectral Projected Gradient Method (SPG) -- Numerical Study-SPG: Quadratic Interpolation versus Cubic Interpolation -- 12.5 L-BFGS with Simple Bounds (L-BFGS-B) -- Numerical Study: L-BFGS-B Versus SPG -- 12.6 Truncated Newton with Simple Bounds (TNBC) -- 12.7 Applications -- Application A1 (Elastic-Plastic Torsion) -- Application A2 (Pressure Distribution in a Journal Bearing) -- Application A3 (Optimal Design with Composite Materials) -- Application A4 (Steady-State Combustion) -- Application A6 (Inhomogeneous Superconductors: 1-D Ginzburg-Landau) -- 13: Quadratic Programming -- 13.1 Equality Constrained Quadratic Programming -- Factorization of the Full KKT System -- The Schur-Complement Method -- The Null-Space Method -- Large-Scale Problems -- The Conjugate Gradient Applied to the Reduced System -- The Projected Conjugate Gradient Method -- 13.2 Inequality Constrained Quadratic Programming -- The Primal Active-Set Method -- An Algorithm for Positive Definite Hessian -- Reduced Gradient for Inequality Constraints -- The Reduced Gradient for Simple Bounds -- The Primal-Dual Active-Set Method -- 13.3 Interior Point Methods -- Stepsize Selection -- 13.4 Methods for Convex QP Problems with Equality Constraints -- 13.5 Quadratic Programming with Simple Bounds: The Gradient Projection Method -- The Cauchy Point -- Subproblem Minimization -- 13.6 Elimination of Variables -- 14: Penalty and Augmented Lagrangian Methods -- 14.1 The Quadratic Penalty Method -- 14.2 The Nonsmooth Penalty Method -- 14.3 The Augmented Lagrangian Method. 14.4 Criticism of the Penalty and Augmented Lagrangian Methods -- 14.5 A Penalty-Barrier Algorithm (SPENBAR) -- The Penalty-Barrier Method -- Global Convergence -- Numerical Study-SPENBAR: Solving Applications from the LACOP Collection -- 14.6 The Linearly Constrained Augmented Lagrangian (MINOS) -- MINOS for Linear Constraints -- Numerical Study: MINOS for Linear Programming -- MINOS for Nonlinear Constraints -- Numerical Study-MINOS: Solving Applications from the LACOP Collection -- 15: Sequential Quadratic Programming -- 15.1 A Simple Approach to SQP -- 15.2 Reduced-Hessian Quasi-Newton Approximations -- 15.3 Merit Functions -- 15.4 Second-Order Correction (Maratos Effect) -- 15.5 The Line-Search SQP Algorithm -- 15.6 The Trust-Region SQP Algorithm -- 15.7 Sequential Linear-Quadratic Programming (SLQP) -- 15.8 A SQP Algorithm for Large-Scale-Constrained Optimization (SNOPT) -- 15.9 A SQP Algorithm with Successive Error Restoration (NLPQLP) -- 15.10 Active-Set Sequential Linear-Quadratic Programming (KNITRO/ACTIVE) -- 16: Primal Methods: The Generalized Reduced Gradient with Sequential Linearization -- 16.1 Feasible Direction Methods -- 16.2 Active Set Methods -- 16.3 The Gradient Projection Method -- 16.4 The Reduced Gradient Method -- 16.5 The Convex Simplex Method -- 16.6 The Generalized Reduced Gradient Method (GRG) -- 16.7 GRG with Sequential Linear or Sequential Quadratic Programming (CONOPT) -- 17: Interior-Point Methods -- 17.1 Prototype of the Interior-Point Algorithm -- 17.2 Aspects of the Algorithmic Developments -- 17.3 Line-Search Interior-Point Algorithm -- 17.4 A Variant of the Line-Search Interior-Point Algorithm -- 17.5 Trust-Region Interior-Point Algorithm -- 17.6 Interior-Point Sequential Linear-Quadratic Programming (KNITRO/INTERIOR) -- 18: Filter Methods -- 18.1 Sequential Linear Programming Filter Algorithm. 18.2 Sequential Quadratic Programming Filter Algorithm. |
| Record Nr. | UNISA-996495169603316 |
|
Andrei Neculai
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| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Modern optimization with R / / Paulo Cortez
| Modern optimization with R / / Paulo Cortez |
| Autore | Cortez Paulo |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (xvii, 254 pages) : illustrations |
| Disciplina | 519.502855133 |
| Collana | Use R! |
| Soggetto topico |
R (Computer program language)
Electronic data processing R (Llenguatge de programació) Processament de dades Optimització matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
3-030-72819-6
9783030728199 3030728196 9783030728182 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996466411303316 |
|
Cortez Paulo
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Modern optimization with R / / Paulo Cortez
| Modern optimization with R / / Paulo Cortez |
| Autore | Cortez Paulo |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (xvii, 254 pages) : illustrations |
| Disciplina | 519.502855133 |
| Collana | Use R! |
| Soggetto topico |
R (Computer program language)
Electronic data processing R (Llenguatge de programació) Processament de dades Optimització matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
3-030-72819-6
9783030728199 3030728196 9783030728182 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910495228603321 |
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Cortez Paulo
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Multi-dimensional control problems : robust approach / / Anurag Jayswal, Preeti, and Savin Treanţa
| Multi-dimensional control problems : robust approach / / Anurag Jayswal, Preeti, and Savin Treanţa |
| Autore | Jayswal Anurag |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (195 pages) |
| Disciplina | 519.3 |
| Collana | Industrial and Applied Mathematics |
| Soggetto topico |
Mathematical optimization
Control theory - Data processing Optimització matemàtica Teoria de control Control de robustesa |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-19-6561-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910624311303321 |
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Jayswal Anurag
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| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Multi-dimensional control problems : robust approach / / Anurag Jayswal, Preeti, and Savin Treanţa
| Multi-dimensional control problems : robust approach / / Anurag Jayswal, Preeti, and Savin Treanţa |
| Autore | Jayswal Anurag |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (195 pages) |
| Disciplina | 519.3 |
| Collana | Industrial and Applied Mathematics |
| Soggetto topico |
Mathematical optimization
Control theory - Data processing Optimització matemàtica Teoria de control Control de robustesa |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-19-6561-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996495770403316 |
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Jayswal Anurag
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| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Nonlinear analysis and global optimization / / Themistocles M. Rassias, Panos M. Pardalos, editors
| Nonlinear analysis and global optimization / / Themistocles M. Rassias, Panos M. Pardalos, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (ix, 486 pages) : illustrations |
| Disciplina | 519.3 |
| Collana | Springer optimization and its applications |
| Soggetto topico |
Mathematical optimization
Teories no lineals Optimització matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-61732-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Friction Models in the Framework of Set-Valued and ConvexAnalysis -- 1 Introduction -- 2 The Approach of Moreau and Panagiotopoulos -- 3 Models of Frictions -- 4 Stability Analysis of a Two-Degree-of-Freedom Mechanical System -- References -- A Survey on Markov's Theorem on Zeros of Orthogonal Polynomials -- 1 Introduction -- 2 Main Result -- 3 Markov's Theorem and Its Descendants -- 4 Some Applications -- 4.1 Sharp Monotonicity Properties of the Zeros of Orthogonal Polynomials Derived from Corollary 3.4 -- 4.2 Sharp Monotonicity Properties of the Zeros of Orthogonal Polynomials Derived from Corollary 3.5 -- 4.3 Monotonicity of the Zeros of Classical Continuous Orthogonal Polynomials Derived from Corollary 3.1 (Markov's Theorem) -- 4.4 Sharp Monotonicity Properties of the Zeros of Classical Continuous Orthogonal Polynomials Derived from Corollary 3.3 -- 4.5 Monotonicity of the Zeros of Classical Discrete Orthogonal Polynomials Derived from Corollary 3.1 (Markov's Theorem) -- References -- A Review of Two Network Curvature Measures -- 1 Introduction -- 2 Gromov-Hyperbolic Curvature -- 2.1 Topological Characteristics of Gromov-Hyperbolicity Measure -- 2.2 Gromov Curvature of Real-World Networks -- 2.3 Efficient Computation of Gromov Curvature -- 2.4 Algorithmic Implications of Small Gromov Curvature -- 2.5 Statistical Validation of Gromov Curvature via ``Scaled'' Version -- 3 Geometric Curvature -- 3.1 Basic Topological Concepts -- 3.2 Topological Association of Networks with a Complex -- 3.3 Defining Geometric Curvatures for Elementary Components of Given Graph -- 1-Complex-Based Geometric Curvature for a Pair of Nodes -- 2-Complex-Based Geometric Curvature for a Pair of Nodes -- Higher-Dimensional Geometric Curvature for a Pair of Nodes -- 3.4 Overall (Scalar) Curvature Value for a Network.
3.5 Computation of Geometric Curvatures -- 3.6 Real-World Networks and Geometric Curvatures -- 3.7 Statistical Validations for all Curvature Measures -- 4 Two Applications of Curvature Analysis of Graphs -- 4.1 Detecting Critical Elementary Components of Networks -- 4.2 Detecting Change Points in Dynamic Networks -- 5 Conclusion -- References -- A Frictional Dynamic Thermal Contact Problem with Normal Compliance and Damage -- 1 Introduction -- 2 The Contact Problem -- 3 Proof of Theorem 1 -- 4 Analysis of a Numerical Scheme -- 5 Numerical Computations -- References -- Mixed Concave-Convex Sub-Superlinear Schrödinger Equation: Survey and Development of Some New Cases -- 1 Introduction -- 2 The Stationary Problem -- 2.1 Mixed Sub-linear Defocusing Case 0< -- p< -- q< -- 1 -- 2.2 The Defocusing Sub-linear Case p=1, 0< -- q< -- 1 -- 2.3 The Mixed Sub-linear/Linear Case 0< -- p< -- 1 and q=1 -- 2.4 A Mixed Sub-linear Defocusing Case o< -- q< -- p< -- 1 -- 2.5 A Mixed Sub-linear/Super-Linear Defocusing Case 0< -- p< -- 1< -- q -- 2.6 Mixed Super-Linear/Sub-linear Defocusing Case 0< -- q< -- 1< -- p -- 2.7 Mixed Linear/Super-Linear Defocusing Case p=1 and q> -- 1 -- 2.8 Mixed Super-Linear/Linear Defocusing Case q=1 and p> -- 1 -- 2.9 Mixed Super-Linear/Super-Linear Defocusing Case 1< -- p< -- q -- 2.10 Mixed Super-Linear/Super-Linear Defocusing Case 1< -- q< -- p -- 3 Some Graphical Illustrations -- 3.1 Example 1: The Mixed Sub-linear Case 0< -- p< -- q< -- 1 -- 3.2 Example 2: The Mixed Linear/Sub-linear Case p=1 and 0< -- q< -- 1 -- 3.3 Example 3: The Mixed Sub-linear/Sub-linear Case 0< -- q< -- p< -- 1 -- 3.4 Example 4: The Mixed Sub-linear/Linear Case 0< -- p< -- 1 and q=1 -- 4 Conclusion -- References -- An Optimization Model for a Network of Organ Transplants with Uncertain Availability. 1 Introduction -- 2 The Mathematical Model -- 3 Variational Inequality -- 4 Numerical Examples -- 4.1 Example 1 -- Example 1.2 -- 4.2 Example 2 -- 5 Conclusions -- References -- Algebraic Based Techniques as Decision Making Tools -- 1 Introduction -- 2 Preliminaries -- 3 Median Algebras -- 4 Median Homomorphisms as Consensus Procedures -- 4.1 Consensus Over Trees -- 4.2 Consensus Over Hypercube-Free Median Algebras -- 4.3 Consensus Over Weak Orders -- 5 Application -- 6 Conclusions -- References -- Norm Estimates for the Composite Operators -- 1 Introduction -- 2 Local Integrability -- 3 Global Integrability -- References -- A Variational Inequality Based Stochastic Approximation for Inverse Problems in Stochastic Partial Differential Equations -- 1 Introduction -- 2 Stochastic Approximation for Variational Inequalities -- 3 Stochastic Approximation for Inverse Problems -- 3.1 Optimization Formulation of the Inverse Problem -- 3.2 Discrete Formulas -- 4 Computational Experiments -- 5 Concluding Remarks -- References -- An Iterative Method for a Common Solution of Split Generalized Equilibrium Problems and Fixed Points of a Finite Family of Nonexpansive Mapping -- 1 Introduction -- 2 Preliminaries -- 3 The Proposed Method and Some Properties -- 4 Numerical Example -- 5 Conclusions -- References -- Periodic Solutions Around the Out-of-Plane Equilibrium Points in the Restricted Three-Body Problem with Radiation and Angular Velocity Variation -- 1 Introduction -- 2 Equations of Motion -- 3 Out-of-Plane Equilibrium Points -- 4 Spatial Periodic Orbits Around the Out-of-Plane Equilibria -- 4.1 The Analytical Approximation -- 4.2 The Numerical Approximation -- 5 Discussion and Conclusion -- References -- Optimal Lot Size with Partial Backlogging Under the Occurrence of Imperfect Quality Items -- 1 Introduction -- 2 Preliminaries -- 2.1 Assumptions. 2.2 Notation -- 3 Model Formulation and Optimal Policy Determination -- 3.1 Cycle Length -- 3.2 Total Profit Formulation -- 3.3 Optimal Policy -- 3.4 Special Cases -- 4 Numerical Comparisons -- 5 Cost Penalty of Employing the EOQ with Partial Backorders -- 6 Conclusions -- Appendix -- References -- Error Analysis Through Energy Minimization and Stability Properties of Exponential Integrators -- 1 Introduction and Motivation -- 2 Exponential Integrators for Hamiltonian Systems -- 3 High-Order Exponential Variational Integrators -- 4 Stability Analysis of Exponential Variational Integrators -- 4.1 Exponential Variational Integrator for S=2 -- 4.2 Exponential Variational Integrator for S=3 -- 5 Error Analysis from Testing the Variational Integrators in Hamiltonian Systems -- 6 Conclusions -- References -- A Degenerate Kirchhoff-Type Inclusion Problem with NonlocalOperator -- 1 Introduction -- 2 Mathematical Background -- 3 Statement of Main Result -- 4 Truncated Problems -- 5 Mountain-Pass Geometry -- 6 Palais-Smale Condition -- 7 Proof of Theorem 2 and Example -- References -- Competition for Medical Supplies Under Stochastic Demand in the Covid-19 Pandemic: A Generalized Nash Equilibrium Framework -- 1 Introduction -- 2 Literature Review and Our Contributions -- 3 The Generalized Nash Equilibrium Network Model for Medical Supplies Under Stochastic Demand -- 3.1 Illustrative Examples -- 4 Qualitative Properties and the Algorithm -- The Modified Projection Method -- 5 Numerical Examples -- 6 Summary and Conclusions and Suggestions for Future Research -- References -- Relative Strongly Exponentially Convex Functions -- 1 Introduction -- 2 Preliminary Results -- 3 Main Results -- 4 Conclusion -- References -- Properties of Exponentially m-Convex Functions -- 1 Introduction -- 2 Preliminary Results -- 3 Main Results -- 4 Conclusion -- References. Natural vs. Artificial Topologies on a Relativistic Spacetime -- 1 Motivation: The Topologization Problem -- 2 What Is (or Should be) the Role of Spacetime Topology? -- 3 Singularities, Naked Singularities and a Kind of Unexpected Gravitational Time Delay Effects -- 4 A Duality Between Timelike-Spacelike Events: Between ``Chronos'' and ``Choros'' -- 5 Questions -- References -- On the Approximation of Monotone Variational Inequalities in Lp Spaces with Probability Measure -- 1 Introduction -- 2 Regularization of Random Variational Inequalities -- 2.1 Random Variational Inequalities in Probabilistic Lebesgue Spaces -- 2.2 A Functional Approximation Scheme for the Random Variational Inequality -- 2.3 Implementation -- 3 Application to the Traffic Network Equilibrium Problem with Random Data -- 3.1 An Outline of the Traffic Network Equilibrium Problem -- 3.2 The Stochastic VI Formulation of the Traffic Network Equilibrium Problem -- 3.3 Numerical Experiments -- References -- Operator Factorization and Solution of Second-Order Nonlinear Difference Equations with Variable Coefficients and Multipoint Constraints -- 1 Introduction -- 2 Factorization Method -- 3 Second-Order Nonlinear Difference Equations -- 3.1 Type I -- 3.2 Type II -- References -- An Invitation to the Study of a Uniqueness Problem -- Reference -- Schrödinger Equations in Nonlinear Optics -- 1 Introduction -- 2 Some Lemmas -- 3 Proof of Theorem 2 -- 4 Proof of Theorem 3 -- References -- Ekeland Variational Principles in 2-Local Branciari Metric Spaces -- 1 Introduction -- 2 Dependent Choice Principles -- 3 Conv-Cauchy Structures -- 4 Local and 2-Local Branciari Metric Spaces -- 5 Main Result -- 6 Equivalence Statements -- References. |
| Record Nr. | UNINA-9910483922303321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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