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An optimization primer / / Johannes O. Royset and Roger J.-B. Wets
An optimization primer / / Johannes O. Royset and Roger J.-B. Wets
Autore Royset Johannes O.
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (692 pages)
Disciplina 519.6
Collana Springer Series in Operations Research and Financial Engineering
Soggetto topico Optimització matemàtica
Mathematical optimization
Soggetto genere / forma Llibres electrònics
ISBN 3-030-76275-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- How to Read the Book -- Supporting Material -- Acknowledgements -- Contents -- 1 PRELUDE -- 1.A The Mathematical Curtain Rise -- 1.B Data Smoothing -- 1.C Optimization under Uncertainty -- 1.D Convex Analysis -- 1.E Estimation and Classification -- 1.F Gradient Descent Method -- 1.G Newton's Method -- 1.H Acceleration and Regularization -- 1.I Quasi-Newton Methods -- 1.J Coordinate Descent Algorithms -- 2 CONVEX OPTIMIZATION -- 2.A Formulations -- 2.B Subderivatives and Subgradients -- 2.C Subgradient Calculus -- 2.D Proximal Gradient Methods -- 2.E Linear Constraints -- 2.F Karush-Kuhn-Tucker Condition -- 2.G Interior-Point Method -- 2.H Support Vector Machines -- 2.I Subgradient Method -- 2.J Conic Constraints -- 2.K Polyhedral Analysis -- 3 OPTIMIZATION UNDER UNCERTAINTY -- 3.A Product Mix Optimization -- 3.B Expectation Functions -- 3.C Risk Modeling -- 3.D Models of Uncertainty -- 3.E Risk-Adaptive Design -- 3.F Optimality in Stochastic Optimization -- 3.G Stochastic Gradient Descent -- 3.H Simple Recourse Problems -- 3.I Control of Water Pollution -- 3.J Linear Recourse Problems -- 3.K Network Capacity Expansion -- 4 MINIMIZATION PROBLEMS -- 4.A Formulations -- 4.B Network Design and Operation -- 4.C Epigraphical Approximation Algorithm -- 4.D Constraint Softening -- 4.E Set Analysis -- 4.F Robotic Path Planning -- 4.G Tangent and Normal Cones I -- 4.H Tangent and Normal Cones II -- 4.I Subdifferentiability -- 4.J Optimality Conditions -- 4.K SQP and Interior-Point Methods -- 5 PERTURBATION AND DUALITY -- 5.A Rockafellians -- 5.B Quantitative Stability -- 5.C Lagrangians and Dual Problems -- 5.D Lagrangian Relaxation -- 5.E Saddle Points -- 5.F Strong Duality -- 5.G Reformulations -- 5.H L-Shaped Method -- 5.I Monitoring Functions -- 5.J Lagrangian Finite-Generation Method -- 6 WITHOUT CONVEXITY OR SMOOTHNESS.
6.A Second-Order Analysis -- 6.B Augmented Lagrangians -- 6.C Epigraphical Nesting -- 6.D Optimality Conditions -- 6.E Sup-Projections -- 6.F Proximal Composite Method -- 6.G Design of Multi-Component Systems -- 6.H Difference-of-Convex Functions -- 6.I DC in Regression and Classification -- 6.J Approximation Errors -- 7 GENERALIZED EQUATIONS -- 7.A Formulations -- 7.B Equilibrium in Energy Markets -- 7.C Traffic Equilibrium -- 7.D Reformulation as Minimization Problems -- 7.E Projection Methods -- 7.F Nonsmooth Newton-Raphson Algorithm -- 7.G Continuity of Set-Valued Mappings -- 7.H Graphical Approximation Algorithm -- 7.I Consistent Approximations -- 7.J Approximation Errors -- 8 RISK MODELING AND SAMPLE AVERAGES -- 8.A Estimation of Optimality Gaps -- 8.B Risk and Regret -- 8.C Risk-Adaptive Data Analytics -- 8.D Duality -- 8.E Subgradients of Functionals -- 8.F Residual Risk and Surrogates -- 8.G Sample Average Approximations -- 8.H Concentration Inequalities -- 8.I Diametrical Stochastic Optimization -- 9 GAMES AND MINSUP PROBLEMS -- 9.A Nash Games -- 9.B Formulation as Minsup Problems -- 9.C Bifunctions and Solutions -- 9.D Lopsided Approximation Algorithm -- 9.E Lop-Convergence I -- 9.F Lop-Convergence II -- 9.G Approximation of Games -- 9.H Walras Barter Model -- 10 DECOMPOSITION -- 10.A Proximal Alternating Gradient Method -- 10.B Linkage Constraints -- 10.C Progressive Decoupling Algorithm -- 10.D Local Elicitation -- 10.E Decoupling in Stochastic Optimization -- 10.F Strong Monotonicity -- 10.G Variational Convexity and Elicitation -- 10.H Nonlinear Linkage -- References -- Index.
Record Nr. UNISA-996466550103316
Royset Johannes O.  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
An optimization primer / / Johannes O. Royset and Roger J.-B. Wets
An optimization primer / / Johannes O. Royset and Roger J.-B. Wets
Autore Royset Johannes O.
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (692 pages)
Disciplina 519.6
Collana Springer Series in Operations Research and Financial Engineering
Soggetto topico Optimització matemàtica
Mathematical optimization
Soggetto genere / forma Llibres electrònics
ISBN 3-030-76275-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- How to Read the Book -- Supporting Material -- Acknowledgements -- Contents -- 1 PRELUDE -- 1.A The Mathematical Curtain Rise -- 1.B Data Smoothing -- 1.C Optimization under Uncertainty -- 1.D Convex Analysis -- 1.E Estimation and Classification -- 1.F Gradient Descent Method -- 1.G Newton's Method -- 1.H Acceleration and Regularization -- 1.I Quasi-Newton Methods -- 1.J Coordinate Descent Algorithms -- 2 CONVEX OPTIMIZATION -- 2.A Formulations -- 2.B Subderivatives and Subgradients -- 2.C Subgradient Calculus -- 2.D Proximal Gradient Methods -- 2.E Linear Constraints -- 2.F Karush-Kuhn-Tucker Condition -- 2.G Interior-Point Method -- 2.H Support Vector Machines -- 2.I Subgradient Method -- 2.J Conic Constraints -- 2.K Polyhedral Analysis -- 3 OPTIMIZATION UNDER UNCERTAINTY -- 3.A Product Mix Optimization -- 3.B Expectation Functions -- 3.C Risk Modeling -- 3.D Models of Uncertainty -- 3.E Risk-Adaptive Design -- 3.F Optimality in Stochastic Optimization -- 3.G Stochastic Gradient Descent -- 3.H Simple Recourse Problems -- 3.I Control of Water Pollution -- 3.J Linear Recourse Problems -- 3.K Network Capacity Expansion -- 4 MINIMIZATION PROBLEMS -- 4.A Formulations -- 4.B Network Design and Operation -- 4.C Epigraphical Approximation Algorithm -- 4.D Constraint Softening -- 4.E Set Analysis -- 4.F Robotic Path Planning -- 4.G Tangent and Normal Cones I -- 4.H Tangent and Normal Cones II -- 4.I Subdifferentiability -- 4.J Optimality Conditions -- 4.K SQP and Interior-Point Methods -- 5 PERTURBATION AND DUALITY -- 5.A Rockafellians -- 5.B Quantitative Stability -- 5.C Lagrangians and Dual Problems -- 5.D Lagrangian Relaxation -- 5.E Saddle Points -- 5.F Strong Duality -- 5.G Reformulations -- 5.H L-Shaped Method -- 5.I Monitoring Functions -- 5.J Lagrangian Finite-Generation Method -- 6 WITHOUT CONVEXITY OR SMOOTHNESS.
6.A Second-Order Analysis -- 6.B Augmented Lagrangians -- 6.C Epigraphical Nesting -- 6.D Optimality Conditions -- 6.E Sup-Projections -- 6.F Proximal Composite Method -- 6.G Design of Multi-Component Systems -- 6.H Difference-of-Convex Functions -- 6.I DC in Regression and Classification -- 6.J Approximation Errors -- 7 GENERALIZED EQUATIONS -- 7.A Formulations -- 7.B Equilibrium in Energy Markets -- 7.C Traffic Equilibrium -- 7.D Reformulation as Minimization Problems -- 7.E Projection Methods -- 7.F Nonsmooth Newton-Raphson Algorithm -- 7.G Continuity of Set-Valued Mappings -- 7.H Graphical Approximation Algorithm -- 7.I Consistent Approximations -- 7.J Approximation Errors -- 8 RISK MODELING AND SAMPLE AVERAGES -- 8.A Estimation of Optimality Gaps -- 8.B Risk and Regret -- 8.C Risk-Adaptive Data Analytics -- 8.D Duality -- 8.E Subgradients of Functionals -- 8.F Residual Risk and Surrogates -- 8.G Sample Average Approximations -- 8.H Concentration Inequalities -- 8.I Diametrical Stochastic Optimization -- 9 GAMES AND MINSUP PROBLEMS -- 9.A Nash Games -- 9.B Formulation as Minsup Problems -- 9.C Bifunctions and Solutions -- 9.D Lopsided Approximation Algorithm -- 9.E Lop-Convergence I -- 9.F Lop-Convergence II -- 9.G Approximation of Games -- 9.H Walras Barter Model -- 10 DECOMPOSITION -- 10.A Proximal Alternating Gradient Method -- 10.B Linkage Constraints -- 10.C Progressive Decoupling Algorithm -- 10.D Local Elicitation -- 10.E Decoupling in Stochastic Optimization -- 10.F Strong Monotonicity -- 10.G Variational Convexity and Elicitation -- 10.H Nonlinear Linkage -- References -- Index.
Record Nr. UNINA-9910556881103321
Royset Johannes O.  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Optimization, variational analysis and applications : IFSOVAA-2020, Varanasi, India, February 2-4 / / edited by Vivek Laha, Pierre Maréchal, and S. K. Mishra
Optimization, variational analysis and applications : IFSOVAA-2020, Varanasi, India, February 2-4 / / edited by Vivek Laha, Pierre Maréchal, and S. K. Mishra
Pubbl/distr/stampa Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (444 pages)
Disciplina 620.00151564
Collana Springer Proceedings in Mathematics and Statistics
Soggetto topico Calculus of variations
Mathematical optimization
Optimització matemàtica
Càlcul de variacions
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 981-16-1819-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Foreword -- Preface -- Contents -- Contributors -- 1 Linear and Pascoletti-Serafini Scalarizations in Unified Set Optimization -- 1.1 Introduction -- 1.2 Preliminaries -- 1.3 Scalarization -- 1.4 Linear Scalarizations -- 1.5 Pascoletti-Serafini Scalarization Scheme -- 1.6 Conclusions -- References -- 2 A Gradient-Free Method for Multi-objective Optimization Problem -- 2.1 Introduction -- 2.2 Notations and Preliminaries -- 2.3 Gradient-Free Method for MOP -- 2.3.1 Modified Nelder-Mead Algorithm -- 2.4 Numerical Illustrations and Performance Assessment -- 2.5 Performance Profile -- 2.6 Conclusions -- References -- 3 The New Butterfly Relaxation Method for Mathematical Programs with Complementarity Constraints -- 3.1 Introduction -- 3.2 Preliminaries -- 3.2.1 Non-Linear Programming -- 3.2.2 Mathematical Programs with Complementarity Constraints -- 3.3 The Butterfly Relaxation Method -- 3.4 Theoretical Properties -- 3.4.1 Convergence -- 3.4.2 Existence of Lagrange Multipliers for the Relaxed Sub-Problems -- 3.4.3 Convergence of the Epsilon-Stationary Points -- 3.5 Numerical Results -- 3.5.1 On the Implementation of the Butterfly Relaxation -- 3.5.2 Comparison of the Relaxation Methods -- 3.6 Concluding Remarks -- Appendix -- 3.7 Proof of a Technical Lemma -- References -- 4 Copositive Optimization and Its Applications in Graph Theory -- 4.1 Introduction -- 4.1.1 Quadratic Programming Problem with Binary and Continuous Variables -- 4.1.2 Fractional Quadratic Optimization Problem -- 4.1.3 More on Nonconvex Quadratic Programming Problems -- 4.1.4 Quadratic Optimization Problem and the Concept of Lifted Problem -- 4.1.5 Quadratic Optimization Problem and the Role of Special Matrix Classes -- 4.2 Applications of Copositive Optimization in Graph Theory -- 4.2.1 Maximum Weight Clique Problem.
4.3 The Notion of Transfinite Diameter in a Finite Metric Space and Copositive Optimization Problem -- 4.4 Symmetric Bimatrix Game as a Copositive Optimization Problem -- References -- 5 Hermite-Hadamard Type Inequalities For Functions Whose Derivatives Are Strongly η-Convex Via Fractional Integrals -- 5.1 Introduction -- 5.2 Preliminaries -- 5.3 Main Results -- 5.3.1 Application to Means -- 5.4 Conclusion -- References -- 6 Set Order Relations, Set Optimization, and Ekeland's Variational Principle -- 6.1 Introduction -- 6.2 Preliminaries -- 6.3 Set Order Relations -- 6.3.1 Set Order Relations in Terms of the Minkowski Difference -- 6.3.2 Set Order Relations with Respect to Variable Domination Structures -- 6.4 Nonlinear Scalarization Functions -- 6.4.1 Weighted Set Order Relations -- 6.5 Solution Concepts in Set Optimization -- 6.5.1 Solution Concepts in Set Optimization with Respect to Variable Domination Structures -- 6.6 Existence of Solutions -- 6.6.1 Generalized Semicontinuity for Set-Valued Maps -- 6.6.2 Existence of Solutions in Set Optimization Problems -- 6.6.3 Relation Between Minimal Solutions with Respect to Vector and Set Approach -- 6.7 Ekeland's Variational Principle for Set-Valued Maps -- 6.7.1 A Minimal Element Theorem and Ekeland's Principle with Mixed Set Order Relations -- 6.7.2 Ekeland's Variational Principle for Set-Valued Maps in Quasi-Metric Spaces -- References -- 7 Characterizations and Generating Efficient Solutions to Interval Optimization Problems -- 7.1 Introduction -- 7.1.1 Literature Survey -- 7.1.2 Motivation and Contribution -- 7.1.3 Organization -- 7.2 Preliminaries -- 7.2.1 Interval Arithmetic -- 7.2.2 Interval-Valued Functions and Interval Optimization Problems -- 7.3 Characterizations of Efficient Solutions -- 7.3.1 Bi-objective Characterization -- 7.3.2 Saddle Point Characterization.
7.4 Generating the Complete Efficient Solution Set of IOPs -- 7.5 Conclusion -- References -- 8 Unconstrained Reformulation of Sequential Quadratic Programming and Its Application in Convex Optimization -- 8.1 Introduction -- 8.2 Mathematical Backgrounds -- 8.3 Unconstrained Reformulation of the Convex Programming -- 8.4 Geometrical Illustration -- 8.5 Numerical Examples -- 8.6 Conclusion -- References -- 9 A Note on Quadratic Penalties for Linear Ill-Posed Problems: From Tikhonov Regularization to Mollification -- 9.1 Introduction -- 9.2 Generalizing Tikhonov Regularization -- 9.3 Target Objects -- 9.4 Conclusion -- References -- 10 A New Regularization Method for Linear Exponentially Ill-Posed Problems -- 10.1 Introduction -- 10.2 The New Regularization Method -- 10.3 Optimality Results -- 10.3.1 Optimality Under Logarithmic Source Conditions -- 10.3.2 Optimality Under General Source Conditions -- 10.4 A Framework for Comparison -- 10.4.1 Computation of the Regularized Solution xα,nrmδ -- 10.4.2 Tikhonov Versus New Method -- 10.4.3 Spectral Cut-Off Versus New Method -- 10.4.4 Showalter Versus New Method -- 10.4.5 Conjugate Gradient Versus New Method -- 10.5 Numerical Illustration -- 10.6 Parameter Selection Rules -- 10.7 Conclusion -- 10.8 Appendix -- References -- 11 On Minimax Programming with Vanishing Constraints -- 11.1 Introduction -- 11.2 Optimality Conditions -- 11.3 Duality Results -- 11.4 Applications to Multiobjective Optimization -- 11.5 Conclusions -- References -- 12 On Minty Variational Principle for Nonsmooth Interval-Valued Multiobjective Programming Problems -- 12.1 Introduction -- 12.1.1 The Proposed Work -- 12.2 Definition and Preliminaries -- 12.3 Relationship Among (NIVMPP), (ASVI) and (AMVI) -- 12.4 Conclusions -- References.
13 On Constraint Qualifications for Multiobjective Optimization Problems with Switching Constraints -- 13.1 Introduction -- 13.2 Preliminaries -- 13.3 Constraint Qualifications for Multiobjective Optimization Problems with Switching Constraint -- 13.3.1 A Generalized Guignard and Abadie CQ for MOPSC -- 13.4 Stationary Conditions for MOPSC -- 13.5 Sufficient Optimality Conditions for the MOPSC -- 13.6 Duality -- 13.7 Future Research Work -- References -- 14 Optimization of Physico-Chemical Parameters for the Production of Endoxylanase Using Combined Response Surface Method and Genetic Algorithm -- 14.1 Introduction -- 14.2 Materials and Methods -- 14.2.1 Microorganism and Material -- 14.2.2 Inoculum Development for Endoxylanase Production -- 14.2.3 Production Medium -- 14.2.4 Substrate and Solid-State Fermentation -- 14.2.5 Enzyme Extraction and Assay -- 14.2.6 Experimental Design -- 14.2.7 Statistical Analysis -- 14.2.8 Genetic Algorithm -- 14.3 Results and Discussion -- 14.4 Conclusion -- References -- 15 Optimal Duration of Integrated Segment Specific and Mass Promotion Activities for Durable Technology Products: A Differential Evolution Approach -- 15.1 Introduction -- 15.2 Literature Review -- 15.2.1 Literature Gap and Research Motivation -- 15.3 Conceptual Framework and Model Development -- 15.3.1 System of Notations -- 15.3.2 Model Development -- 15.4 Solution Methodology -- 15.5 Case Study -- 15.5.1 Data Description -- 15.6 Results and Discussions -- 15.7 Managerial and Theoretical Implications -- 15.8 Conclusion and Future Scope -- References -- 16 A Secure RGB Image Encryption Algorithm in Optimized Virtual Planet Domain -- 16.1 Introduction -- 16.2 Optimized 4D Hyper-chaotic System and Virtual Planet Domain -- 16.2.1 4D Hyper-chaotic System -- 16.2.2 Teaching-learning-Based Optimization (TLBO) Algorithm.
16.2.3 Virtual Planet Domain Encoding Process -- 16.2.4 Virtual Planet Domain Encoding Process -- 16.3 Proposed Encryption and Decryption Algorithm -- 16.3.1 Encryption Procedure -- 16.3.2 Decryption Procedure -- 16.4 Statistical Analysis -- 16.4.1 Mean Square Error -- 16.4.2 Peak Signal-to-Noise Ratio -- 16.4.3 Histogram Analysis -- 16.4.4 Correlation Analysis -- 16.4.5 Cropping Analysis -- 16.4.6 Pixel Sensitivity and Key Sensitivity -- 16.4.7 Robustness Against Known Plain-Image and Chosen Cipher-Image Attacks -- 16.4.8 Differential Analysis -- 16.4.9 Information Entropy -- 16.4.10 Efficiency of Proposed Encoding Technique -- 16.4.11 Run Time of the Algorithm -- 16.5 Comparison -- 16.6 Conclusion -- References -- 17 Identification and Analysis of Key Sustainable Criteria for Third Party Reverse Logistics Provider Selection Using the Best Worst Method -- 17.1 Introduction -- 17.2 Literature Review -- 17.2.1 Outsourcing in Reverse Logistics -- 17.2.2 Sustainable Performance Criteria for Provider Selection in Reverse Logistics -- 17.2.3 Research Contribution -- 17.3 Methodology used for Selection and Evaluation of Criteria -- 17.3.1 Identification of Criteria -- 17.3.2 Delphi Technique for Identification of Key Sustainable Criteria for 3PRLP Evaluation -- 17.3.3 Best Worst Method for Ranking of Key Sustainable Criteria for 3PRLP Evaluation -- 17.4 Application of the Proposed Methodology -- 17.4.1 Identification of Key Criteria Using Delphi Technique -- 17.4.2 Evaluation of Key Sustainable Criteria Using Best Worst Method -- 17.5 Result Discussion -- 17.6 Conclusion -- References -- 18 Efficiency Assessment Through Peer Evaluation and Benchmarking: A Case Study of a Retail Chain Using DEA -- 18.1 Introduction -- 18.2 Literature Review -- 18.2.1 Contribution of the Study -- 18.3 Problem Definition -- 18.4 Methodology.
18.4.1 Cross-Efficiency Assessment.
Record Nr. UNINA-9910495192603321
Singapore : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Optimization, variational analysis and applications : IFSOVAA-2020, Varanasi, India, February 2-4 / / edited by Vivek Laha, Pierre Maréchal, and S. K. Mishra
Optimization, variational analysis and applications : IFSOVAA-2020, Varanasi, India, February 2-4 / / edited by Vivek Laha, Pierre Maréchal, and S. K. Mishra
Pubbl/distr/stampa Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (444 pages)
Disciplina 620.00151564
Collana Springer Proceedings in Mathematics and Statistics
Soggetto topico Calculus of variations
Mathematical optimization
Optimització matemàtica
Càlcul de variacions
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 981-16-1819-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Foreword -- Preface -- Contents -- Contributors -- 1 Linear and Pascoletti-Serafini Scalarizations in Unified Set Optimization -- 1.1 Introduction -- 1.2 Preliminaries -- 1.3 Scalarization -- 1.4 Linear Scalarizations -- 1.5 Pascoletti-Serafini Scalarization Scheme -- 1.6 Conclusions -- References -- 2 A Gradient-Free Method for Multi-objective Optimization Problem -- 2.1 Introduction -- 2.2 Notations and Preliminaries -- 2.3 Gradient-Free Method for MOP -- 2.3.1 Modified Nelder-Mead Algorithm -- 2.4 Numerical Illustrations and Performance Assessment -- 2.5 Performance Profile -- 2.6 Conclusions -- References -- 3 The New Butterfly Relaxation Method for Mathematical Programs with Complementarity Constraints -- 3.1 Introduction -- 3.2 Preliminaries -- 3.2.1 Non-Linear Programming -- 3.2.2 Mathematical Programs with Complementarity Constraints -- 3.3 The Butterfly Relaxation Method -- 3.4 Theoretical Properties -- 3.4.1 Convergence -- 3.4.2 Existence of Lagrange Multipliers for the Relaxed Sub-Problems -- 3.4.3 Convergence of the Epsilon-Stationary Points -- 3.5 Numerical Results -- 3.5.1 On the Implementation of the Butterfly Relaxation -- 3.5.2 Comparison of the Relaxation Methods -- 3.6 Concluding Remarks -- Appendix -- 3.7 Proof of a Technical Lemma -- References -- 4 Copositive Optimization and Its Applications in Graph Theory -- 4.1 Introduction -- 4.1.1 Quadratic Programming Problem with Binary and Continuous Variables -- 4.1.2 Fractional Quadratic Optimization Problem -- 4.1.3 More on Nonconvex Quadratic Programming Problems -- 4.1.4 Quadratic Optimization Problem and the Concept of Lifted Problem -- 4.1.5 Quadratic Optimization Problem and the Role of Special Matrix Classes -- 4.2 Applications of Copositive Optimization in Graph Theory -- 4.2.1 Maximum Weight Clique Problem.
4.3 The Notion of Transfinite Diameter in a Finite Metric Space and Copositive Optimization Problem -- 4.4 Symmetric Bimatrix Game as a Copositive Optimization Problem -- References -- 5 Hermite-Hadamard Type Inequalities For Functions Whose Derivatives Are Strongly η-Convex Via Fractional Integrals -- 5.1 Introduction -- 5.2 Preliminaries -- 5.3 Main Results -- 5.3.1 Application to Means -- 5.4 Conclusion -- References -- 6 Set Order Relations, Set Optimization, and Ekeland's Variational Principle -- 6.1 Introduction -- 6.2 Preliminaries -- 6.3 Set Order Relations -- 6.3.1 Set Order Relations in Terms of the Minkowski Difference -- 6.3.2 Set Order Relations with Respect to Variable Domination Structures -- 6.4 Nonlinear Scalarization Functions -- 6.4.1 Weighted Set Order Relations -- 6.5 Solution Concepts in Set Optimization -- 6.5.1 Solution Concepts in Set Optimization with Respect to Variable Domination Structures -- 6.6 Existence of Solutions -- 6.6.1 Generalized Semicontinuity for Set-Valued Maps -- 6.6.2 Existence of Solutions in Set Optimization Problems -- 6.6.3 Relation Between Minimal Solutions with Respect to Vector and Set Approach -- 6.7 Ekeland's Variational Principle for Set-Valued Maps -- 6.7.1 A Minimal Element Theorem and Ekeland's Principle with Mixed Set Order Relations -- 6.7.2 Ekeland's Variational Principle for Set-Valued Maps in Quasi-Metric Spaces -- References -- 7 Characterizations and Generating Efficient Solutions to Interval Optimization Problems -- 7.1 Introduction -- 7.1.1 Literature Survey -- 7.1.2 Motivation and Contribution -- 7.1.3 Organization -- 7.2 Preliminaries -- 7.2.1 Interval Arithmetic -- 7.2.2 Interval-Valued Functions and Interval Optimization Problems -- 7.3 Characterizations of Efficient Solutions -- 7.3.1 Bi-objective Characterization -- 7.3.2 Saddle Point Characterization.
7.4 Generating the Complete Efficient Solution Set of IOPs -- 7.5 Conclusion -- References -- 8 Unconstrained Reformulation of Sequential Quadratic Programming and Its Application in Convex Optimization -- 8.1 Introduction -- 8.2 Mathematical Backgrounds -- 8.3 Unconstrained Reformulation of the Convex Programming -- 8.4 Geometrical Illustration -- 8.5 Numerical Examples -- 8.6 Conclusion -- References -- 9 A Note on Quadratic Penalties for Linear Ill-Posed Problems: From Tikhonov Regularization to Mollification -- 9.1 Introduction -- 9.2 Generalizing Tikhonov Regularization -- 9.3 Target Objects -- 9.4 Conclusion -- References -- 10 A New Regularization Method for Linear Exponentially Ill-Posed Problems -- 10.1 Introduction -- 10.2 The New Regularization Method -- 10.3 Optimality Results -- 10.3.1 Optimality Under Logarithmic Source Conditions -- 10.3.2 Optimality Under General Source Conditions -- 10.4 A Framework for Comparison -- 10.4.1 Computation of the Regularized Solution xα,nrmδ -- 10.4.2 Tikhonov Versus New Method -- 10.4.3 Spectral Cut-Off Versus New Method -- 10.4.4 Showalter Versus New Method -- 10.4.5 Conjugate Gradient Versus New Method -- 10.5 Numerical Illustration -- 10.6 Parameter Selection Rules -- 10.7 Conclusion -- 10.8 Appendix -- References -- 11 On Minimax Programming with Vanishing Constraints -- 11.1 Introduction -- 11.2 Optimality Conditions -- 11.3 Duality Results -- 11.4 Applications to Multiobjective Optimization -- 11.5 Conclusions -- References -- 12 On Minty Variational Principle for Nonsmooth Interval-Valued Multiobjective Programming Problems -- 12.1 Introduction -- 12.1.1 The Proposed Work -- 12.2 Definition and Preliminaries -- 12.3 Relationship Among (NIVMPP), (ASVI) and (AMVI) -- 12.4 Conclusions -- References.
13 On Constraint Qualifications for Multiobjective Optimization Problems with Switching Constraints -- 13.1 Introduction -- 13.2 Preliminaries -- 13.3 Constraint Qualifications for Multiobjective Optimization Problems with Switching Constraint -- 13.3.1 A Generalized Guignard and Abadie CQ for MOPSC -- 13.4 Stationary Conditions for MOPSC -- 13.5 Sufficient Optimality Conditions for the MOPSC -- 13.6 Duality -- 13.7 Future Research Work -- References -- 14 Optimization of Physico-Chemical Parameters for the Production of Endoxylanase Using Combined Response Surface Method and Genetic Algorithm -- 14.1 Introduction -- 14.2 Materials and Methods -- 14.2.1 Microorganism and Material -- 14.2.2 Inoculum Development for Endoxylanase Production -- 14.2.3 Production Medium -- 14.2.4 Substrate and Solid-State Fermentation -- 14.2.5 Enzyme Extraction and Assay -- 14.2.6 Experimental Design -- 14.2.7 Statistical Analysis -- 14.2.8 Genetic Algorithm -- 14.3 Results and Discussion -- 14.4 Conclusion -- References -- 15 Optimal Duration of Integrated Segment Specific and Mass Promotion Activities for Durable Technology Products: A Differential Evolution Approach -- 15.1 Introduction -- 15.2 Literature Review -- 15.2.1 Literature Gap and Research Motivation -- 15.3 Conceptual Framework and Model Development -- 15.3.1 System of Notations -- 15.3.2 Model Development -- 15.4 Solution Methodology -- 15.5 Case Study -- 15.5.1 Data Description -- 15.6 Results and Discussions -- 15.7 Managerial and Theoretical Implications -- 15.8 Conclusion and Future Scope -- References -- 16 A Secure RGB Image Encryption Algorithm in Optimized Virtual Planet Domain -- 16.1 Introduction -- 16.2 Optimized 4D Hyper-chaotic System and Virtual Planet Domain -- 16.2.1 4D Hyper-chaotic System -- 16.2.2 Teaching-learning-Based Optimization (TLBO) Algorithm.
16.2.3 Virtual Planet Domain Encoding Process -- 16.2.4 Virtual Planet Domain Encoding Process -- 16.3 Proposed Encryption and Decryption Algorithm -- 16.3.1 Encryption Procedure -- 16.3.2 Decryption Procedure -- 16.4 Statistical Analysis -- 16.4.1 Mean Square Error -- 16.4.2 Peak Signal-to-Noise Ratio -- 16.4.3 Histogram Analysis -- 16.4.4 Correlation Analysis -- 16.4.5 Cropping Analysis -- 16.4.6 Pixel Sensitivity and Key Sensitivity -- 16.4.7 Robustness Against Known Plain-Image and Chosen Cipher-Image Attacks -- 16.4.8 Differential Analysis -- 16.4.9 Information Entropy -- 16.4.10 Efficiency of Proposed Encoding Technique -- 16.4.11 Run Time of the Algorithm -- 16.5 Comparison -- 16.6 Conclusion -- References -- 17 Identification and Analysis of Key Sustainable Criteria for Third Party Reverse Logistics Provider Selection Using the Best Worst Method -- 17.1 Introduction -- 17.2 Literature Review -- 17.2.1 Outsourcing in Reverse Logistics -- 17.2.2 Sustainable Performance Criteria for Provider Selection in Reverse Logistics -- 17.2.3 Research Contribution -- 17.3 Methodology used for Selection and Evaluation of Criteria -- 17.3.1 Identification of Criteria -- 17.3.2 Delphi Technique for Identification of Key Sustainable Criteria for 3PRLP Evaluation -- 17.3.3 Best Worst Method for Ranking of Key Sustainable Criteria for 3PRLP Evaluation -- 17.4 Application of the Proposed Methodology -- 17.4.1 Identification of Key Criteria Using Delphi Technique -- 17.4.2 Evaluation of Key Sustainable Criteria Using Best Worst Method -- 17.5 Result Discussion -- 17.6 Conclusion -- References -- 18 Efficiency Assessment Through Peer Evaluation and Benchmarking: A Case Study of a Retail Chain Using DEA -- 18.1 Introduction -- 18.2 Literature Review -- 18.2.1 Contribution of the Study -- 18.3 Problem Definition -- 18.4 Methodology.
18.4.1 Cross-Efficiency Assessment.
Record Nr. UNISA-996466406803316
Singapore : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Pyomo-optimization modeling in python / / Michael L. Bynum [and seven others]
Pyomo-optimization modeling in python / / Michael L. Bynum [and seven others]
Autore Bynum Michael L.
Edizione [3rd ed.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (231 pages)
Disciplina 003.3
Collana Springer Optimization and Its Applications
Soggetto topico Computer simulation
Mathematical optimization - Computer simulation
Python (Computer program language)
Simulació per ordinador
Optimització matemàtica
Python (Llenguatge de programació)
Soggetto genere / forma Llibres electrònics
ISBN 3-030-68928-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Goals of the Book -- Who Should Read This Book -- Revisions for the Third Edition -- Acknowledgments -- Disclaimers -- Comments and Questions -- Contents -- Chapter 1 Introduction -- 1.1 Modeling Languages for Optimization -- 1.2 Modeling with Pyomo -- 1.2.1 Simple Examples -- 1.2.2 Graph Coloring Example -- 1.2.3 Key Pyomo Features -- Python -- Customizable Capability -- Command-Line Tools and Scripting -- Concrete and Abstract Model Definitions -- Object-Oriented Design -- Expressive Modeling Capability -- Solver Integration -- Open Source -- 1.3 Getting Started -- 1.4 Book Summary -- 1.5 Discussion -- Part I An Introduction to Pyomo -- Chapter 2 Mathematical Modeling and Optimization -- 2.1 Mathematical Modeling -- 2.1.1 Overview -- 2.1.2 A Modeling Example -- 2.2 Optimization -- 2.3 Modeling with Pyomo -- 2.3.1 A Concrete Formulation -- 2.4 Linear and Nonlinear Optimization Models -- 2.4.1 Definition -- 2.4.2 Linear Version -- 2.5 Solving the Pyomo Model -- 2.5.1 Solvers -- 2.5.2 Python Scripts -- Chapter 3 Pyomo Overview -- 3.1 Introduction -- 3.2 The Warehouse Location Problem -- 3.3 Pyomo Models -- 3.3.1 Components for Variables, Objectives, and Constraints -- 3.3.2 Indexed Components -- 3.3.3 Construction Rules -- 3.3.4 A Concrete Model for the Warehouse Location Problem -- 3.3.5 Modeling Components for Sets and Parameters -- Chapter 4 Pyomo Models and Components: An Introduction -- 4.1 An Object-Oriented AML -- 4.2 Common Component Paradigms -- 4.2.1 Indexed Components -- 4.3 Variables -- 4.3.1 Var Declarations -- 4.3.2 Working with Var Objects -- 4.4 Objectives -- 4.4.1 Objective Declarations -- 4.4.2 Working with Objective Objects -- 4.5 Constraints -- 4.5.1 Constraint Declarations -- 4.5.2 Working with Constraint Objects -- 4.6 Set Data -- 4.6.1 Set Declarations -- 4.6.2 Working with Set Objects.
4.7 Parameter Data -- 4.7.1 Param Declarations -- 4.7.2 Working with Param Objects -- 4.8 Named Expressions -- 4.8.1 Expression Declarations -- 4.8.2 Working with Expression Objects -- 4.9 Suffix Components -- 4.9.1 Suffix Declarations -- 4.9.2 Working with Suffixes -- 4.10 Other Modeling Components -- Chapter 5 Scripting Custom Workflows -- 5.1 Introduction -- 5.2 Interrogating the Model -- 5.2.1 The The value Function -- 5.2.2 Accessing Attributes of Indexed Components -- 5.2.2.1 Slicing Over Indices of Components -- 5.2.2.2 Iterating Over All Var Objects on a Model -- 5.3 Modifying Pyomo Model Structure -- 5.4 Examples of Common Scripting Tasks -- 5.4.1 Warehouse Location Loop and Plotting -- 5.4.2 A Sudoku Solver -- Chapter 6 Interacting with Solvers -- 6.1 Introduction -- 6.2 Using Solvers -- 6.3 Investigating the Solution -- 6.3.1 Solver Results -- Part II Advanced Topics -- Chapter 7 Nonlinear Programming with Pyomo -- 7.1 Introduction -- 7.2 Nonlinear Progamming Problems in Pyomo -- 7.2.1 Nonlinear Expressions -- 7.2.2 The Rosenbrock Problem -- 7.3 Solving Nonlinear Programming Formulations -- 7.3.1 Nonlinear Solvers -- 7.3.2 Additional Tips for Nonlinear Programming -- Variable Initialization -- Undefined Evaluations -- Model Singularities and Problem Scaling -- 7.4 Nonlinear Programming Examples -- 7.4.1 Variable Initialization for a Multimodal Function -- 7.4.2 Optimal Quotas for Sustainable Harvesting of Deer -- 7.4.3 Estimation of Infectious Disease Models -- 7.4.4 Reactor Design -- Chapter 8 Structured Modeling with Blocks -- 8.1 Introduction -- 8.2 Block structures -- 8.3 Blocks as Indexed Components -- 8.4 Construction Rules within Blocks -- 8.5 Extracting values from hierarchical models -- 8.6 Blocks Example: Optimal Multi-Period Lot-Sizing -- 8.6.1 A Formulation Without Blocks -- 8.6.2 A Formulation With Blocks.
Chapter 9 Performance: Model Construction and Solver Interfaces -- 9.1 Profiling to Identify Performance Bottlenecks -- 9.1.1 Report Timing -- 9.1.2 TicTocTimer -- 9.1.3 Profilers -- 9.2 Improving Model Construction Performance with LinearExpression -- 9.3 Repeated Solves with Persistent Solvers -- 9.3.1 When to Use a Persistent Solver -- 9.3.2 Basic Usage -- 9.3.3 Working with Indexed Variables and Constraints -- 9.3.4 Additional Performance -- 9.3.5 Example -- 9.4 Sparse Index Sets -- Chapter 10 Abstract Models and Their Solution -- 10.1 Overview -- 10.1.1 Abstract and Concrete Models -- 10.1.2 An Abstract Formulation of Model (H) -- 10.1.3 An Abstract Model for the Warehouse Location Problem -- 10.2 The pyomo Command -- 10.2.1 The help Subcommand -- 10.2.2 The solve Subcommand -- 10.2.2.1 Specifying the Model Object -- 10.2.2.2 Selecting Data with Namespaces -- 10.2.2.3 Customizing Pyomo's Workflow -- 10.2.2.4 Customizing Solver Behavior -- 10.2.2.5 Analyze Solver Results -- 10.2.2.6 Managing Diagnostic Output -- 10.2.3 The convert Subcommand -- 10.3 Data Commands for Abstract Model -- 10.3.1 The set Command -- 10.3.1.1 Simple Sets -- 10.3.1.2 Sets of Tuple Data -- 10.3.1.3 Set Arrays -- 10.3.2 The param Command -- 10.3.2.1 One-dimensional Parameter Data -- 10.3.2.2 Multi-Dimensional Parameter Data -- 10.3.3 The include Command -- 10.3.4 Data Namespaces -- 10.4 Build Components -- Part III Modeling Extensions -- Chapter 11 Generalized Disjunctive Programming -- 11.1 Introduction -- 11.2 Modeling GDP in Pyomo -- 11.3 Expressing logical constraints -- 11.4 Solving GDP models -- 11.4.1 Big-M transformation -- 11.4.2 Hull transformation -- 11.5 A mixing problem with semi-continuous variables -- Chapter 12 Differential Algebraic Equations -- 12.1 Introduction -- 12.2 Pyomo DAE Modeling Components -- 12.3 Solving Pyomo Models with DAEs.
12.3.1 Finite Difference Transformation -- 12.3.2 Collocation Transformation -- 12.4 Additional Features -- 12.4.1 Applying Multiple Discretizations -- 12.4.2 Restricting Control Input Profiles -- 12.4.3 Plotting -- Chapter 13 Mathematical Programs with Equilibrium Constraints -- 13.1 Introduction -- 13.2 Modeling Equilibrium Conditions -- 13.2.1 Complementarity Conditions -- 13.2.2 Complementarity Expressions -- 13.2.3 Modeling Mixed-Complementarity Conditions -- 13.3 MPEC Transformations -- 13.3.1 Standard Form -- 13.3.2 Simple Nonlinear -- 13.3.3 Simple Disjunction -- 13.3.4 AMPL Solver Interface -- 13.4 Solver Interfaces and Meta-Solvers -- 13.4.1 Nonlinear Reformulations -- 13.4.2 Disjunctive Reformulations -- 13.4.3 PATH and the ASL Solver Interface -- 13.5 Discussion -- Appendix A A Brief Python Tutorial -- A.1 Overview -- A.2 Installing and Running Python -- A.3 Python Line Format -- A.4 Variables and Data Types -- A.5 Data Structures -- A.5.1 Strings -- A.5.2 Lists -- A.5.3 Tuples -- A.5.4 Sets -- A.5.5 Dictionaries -- A.6 Conditionals -- A.7 Iterations and Looping -- A.8 Generators and List Comprehensions -- A.9 Functions -- A.10 Objects and Classes -- A.11 Assignment, copy and deepcopy -- A.11.1 References -- A.11.2 Copying -- A.12 Modules -- A.13 Python Resources -- Bibliography -- Index.
Record Nr. UNINA-9910484570003321
Bynum Michael L.  
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Pyomo-optimization modeling in python / / Michael L. Bynum [and seven others]
Pyomo-optimization modeling in python / / Michael L. Bynum [and seven others]
Autore Bynum Michael L.
Edizione [3rd ed.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (231 pages)
Disciplina 003.3
Collana Springer Optimization and Its Applications
Soggetto topico Computer simulation
Mathematical optimization - Computer simulation
Python (Computer program language)
Simulació per ordinador
Optimització matemàtica
Python (Llenguatge de programació)
Soggetto genere / forma Llibres electrònics
ISBN 3-030-68928-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Goals of the Book -- Who Should Read This Book -- Revisions for the Third Edition -- Acknowledgments -- Disclaimers -- Comments and Questions -- Contents -- Chapter 1 Introduction -- 1.1 Modeling Languages for Optimization -- 1.2 Modeling with Pyomo -- 1.2.1 Simple Examples -- 1.2.2 Graph Coloring Example -- 1.2.3 Key Pyomo Features -- Python -- Customizable Capability -- Command-Line Tools and Scripting -- Concrete and Abstract Model Definitions -- Object-Oriented Design -- Expressive Modeling Capability -- Solver Integration -- Open Source -- 1.3 Getting Started -- 1.4 Book Summary -- 1.5 Discussion -- Part I An Introduction to Pyomo -- Chapter 2 Mathematical Modeling and Optimization -- 2.1 Mathematical Modeling -- 2.1.1 Overview -- 2.1.2 A Modeling Example -- 2.2 Optimization -- 2.3 Modeling with Pyomo -- 2.3.1 A Concrete Formulation -- 2.4 Linear and Nonlinear Optimization Models -- 2.4.1 Definition -- 2.4.2 Linear Version -- 2.5 Solving the Pyomo Model -- 2.5.1 Solvers -- 2.5.2 Python Scripts -- Chapter 3 Pyomo Overview -- 3.1 Introduction -- 3.2 The Warehouse Location Problem -- 3.3 Pyomo Models -- 3.3.1 Components for Variables, Objectives, and Constraints -- 3.3.2 Indexed Components -- 3.3.3 Construction Rules -- 3.3.4 A Concrete Model for the Warehouse Location Problem -- 3.3.5 Modeling Components for Sets and Parameters -- Chapter 4 Pyomo Models and Components: An Introduction -- 4.1 An Object-Oriented AML -- 4.2 Common Component Paradigms -- 4.2.1 Indexed Components -- 4.3 Variables -- 4.3.1 Var Declarations -- 4.3.2 Working with Var Objects -- 4.4 Objectives -- 4.4.1 Objective Declarations -- 4.4.2 Working with Objective Objects -- 4.5 Constraints -- 4.5.1 Constraint Declarations -- 4.5.2 Working with Constraint Objects -- 4.6 Set Data -- 4.6.1 Set Declarations -- 4.6.2 Working with Set Objects.
4.7 Parameter Data -- 4.7.1 Param Declarations -- 4.7.2 Working with Param Objects -- 4.8 Named Expressions -- 4.8.1 Expression Declarations -- 4.8.2 Working with Expression Objects -- 4.9 Suffix Components -- 4.9.1 Suffix Declarations -- 4.9.2 Working with Suffixes -- 4.10 Other Modeling Components -- Chapter 5 Scripting Custom Workflows -- 5.1 Introduction -- 5.2 Interrogating the Model -- 5.2.1 The The value Function -- 5.2.2 Accessing Attributes of Indexed Components -- 5.2.2.1 Slicing Over Indices of Components -- 5.2.2.2 Iterating Over All Var Objects on a Model -- 5.3 Modifying Pyomo Model Structure -- 5.4 Examples of Common Scripting Tasks -- 5.4.1 Warehouse Location Loop and Plotting -- 5.4.2 A Sudoku Solver -- Chapter 6 Interacting with Solvers -- 6.1 Introduction -- 6.2 Using Solvers -- 6.3 Investigating the Solution -- 6.3.1 Solver Results -- Part II Advanced Topics -- Chapter 7 Nonlinear Programming with Pyomo -- 7.1 Introduction -- 7.2 Nonlinear Progamming Problems in Pyomo -- 7.2.1 Nonlinear Expressions -- 7.2.2 The Rosenbrock Problem -- 7.3 Solving Nonlinear Programming Formulations -- 7.3.1 Nonlinear Solvers -- 7.3.2 Additional Tips for Nonlinear Programming -- Variable Initialization -- Undefined Evaluations -- Model Singularities and Problem Scaling -- 7.4 Nonlinear Programming Examples -- 7.4.1 Variable Initialization for a Multimodal Function -- 7.4.2 Optimal Quotas for Sustainable Harvesting of Deer -- 7.4.3 Estimation of Infectious Disease Models -- 7.4.4 Reactor Design -- Chapter 8 Structured Modeling with Blocks -- 8.1 Introduction -- 8.2 Block structures -- 8.3 Blocks as Indexed Components -- 8.4 Construction Rules within Blocks -- 8.5 Extracting values from hierarchical models -- 8.6 Blocks Example: Optimal Multi-Period Lot-Sizing -- 8.6.1 A Formulation Without Blocks -- 8.6.2 A Formulation With Blocks.
Chapter 9 Performance: Model Construction and Solver Interfaces -- 9.1 Profiling to Identify Performance Bottlenecks -- 9.1.1 Report Timing -- 9.1.2 TicTocTimer -- 9.1.3 Profilers -- 9.2 Improving Model Construction Performance with LinearExpression -- 9.3 Repeated Solves with Persistent Solvers -- 9.3.1 When to Use a Persistent Solver -- 9.3.2 Basic Usage -- 9.3.3 Working with Indexed Variables and Constraints -- 9.3.4 Additional Performance -- 9.3.5 Example -- 9.4 Sparse Index Sets -- Chapter 10 Abstract Models and Their Solution -- 10.1 Overview -- 10.1.1 Abstract and Concrete Models -- 10.1.2 An Abstract Formulation of Model (H) -- 10.1.3 An Abstract Model for the Warehouse Location Problem -- 10.2 The pyomo Command -- 10.2.1 The help Subcommand -- 10.2.2 The solve Subcommand -- 10.2.2.1 Specifying the Model Object -- 10.2.2.2 Selecting Data with Namespaces -- 10.2.2.3 Customizing Pyomo's Workflow -- 10.2.2.4 Customizing Solver Behavior -- 10.2.2.5 Analyze Solver Results -- 10.2.2.6 Managing Diagnostic Output -- 10.2.3 The convert Subcommand -- 10.3 Data Commands for Abstract Model -- 10.3.1 The set Command -- 10.3.1.1 Simple Sets -- 10.3.1.2 Sets of Tuple Data -- 10.3.1.3 Set Arrays -- 10.3.2 The param Command -- 10.3.2.1 One-dimensional Parameter Data -- 10.3.2.2 Multi-Dimensional Parameter Data -- 10.3.3 The include Command -- 10.3.4 Data Namespaces -- 10.4 Build Components -- Part III Modeling Extensions -- Chapter 11 Generalized Disjunctive Programming -- 11.1 Introduction -- 11.2 Modeling GDP in Pyomo -- 11.3 Expressing logical constraints -- 11.4 Solving GDP models -- 11.4.1 Big-M transformation -- 11.4.2 Hull transformation -- 11.5 A mixing problem with semi-continuous variables -- Chapter 12 Differential Algebraic Equations -- 12.1 Introduction -- 12.2 Pyomo DAE Modeling Components -- 12.3 Solving Pyomo Models with DAEs.
12.3.1 Finite Difference Transformation -- 12.3.2 Collocation Transformation -- 12.4 Additional Features -- 12.4.1 Applying Multiple Discretizations -- 12.4.2 Restricting Control Input Profiles -- 12.4.3 Plotting -- Chapter 13 Mathematical Programs with Equilibrium Constraints -- 13.1 Introduction -- 13.2 Modeling Equilibrium Conditions -- 13.2.1 Complementarity Conditions -- 13.2.2 Complementarity Expressions -- 13.2.3 Modeling Mixed-Complementarity Conditions -- 13.3 MPEC Transformations -- 13.3.1 Standard Form -- 13.3.2 Simple Nonlinear -- 13.3.3 Simple Disjunction -- 13.3.4 AMPL Solver Interface -- 13.4 Solver Interfaces and Meta-Solvers -- 13.4.1 Nonlinear Reformulations -- 13.4.2 Disjunctive Reformulations -- 13.4.3 PATH and the ASL Solver Interface -- 13.5 Discussion -- Appendix A A Brief Python Tutorial -- A.1 Overview -- A.2 Installing and Running Python -- A.3 Python Line Format -- A.4 Variables and Data Types -- A.5 Data Structures -- A.5.1 Strings -- A.5.2 Lists -- A.5.3 Tuples -- A.5.4 Sets -- A.5.5 Dictionaries -- A.6 Conditionals -- A.7 Iterations and Looping -- A.8 Generators and List Comprehensions -- A.9 Functions -- A.10 Objects and Classes -- A.11 Assignment, copy and deepcopy -- A.11.1 References -- A.11.2 Copying -- A.12 Modules -- A.13 Python Resources -- Bibliography -- Index.
Record Nr. UNISA-996466399003316
Bynum Michael L.  
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Separable optimization : theory and methods. / / Stefan M. Stefanov
Separable optimization : theory and methods. / / Stefan M. Stefanov
Autore Stefanov Stefan M.
Edizione [Second edition.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (360 pages)
Disciplina 515
Collana Springer Optimization and Its Applications
Soggetto topico Applied mathematics
Optimització matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 9783030784010
9783030784003
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996466555903316
Stefanov Stefan M.  
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Separable optimization : theory and methods. / / Stefan M. Stefanov
Separable optimization : theory and methods. / / Stefan M. Stefanov
Autore Stefanov Stefan M.
Edizione [Second edition.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (360 pages)
Disciplina 515
Collana Springer Optimization and Its Applications
Soggetto topico Applied mathematics
Optimització matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 9783030784010
9783030784003
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910520079103321
Stefanov Stefan M.  
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Soft computing and optimization : SCOTA 2021, Ranchi, India, March 26-27 / / edited by Syeda Darakhshan Jabeen, Javid Ali, and Oscar Castillo
Soft computing and optimization : SCOTA 2021, Ranchi, India, March 26-27 / / edited by Syeda Darakhshan Jabeen, Javid Ali, and Oscar Castillo
Edizione [1st ed. 2022.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore Pte Ltd, , [2022]
Descrizione fisica 1 online resource (362 pages)
Disciplina 519.3
Collana Springer Proceedings in Mathematics & Statistics
Soggetto topico Mathematical optimization
Soft computing
Informàtica tova
Optimització matemàtica
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 981-19-6406-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto R. Kumari, A. Nigam and S. Pushkar, Classification of MRI images for detecting Alzheimer Disease using Convolutional Neural Network -- S. D. Godwal and S. S. Kanojia, Optimum over Current Relays Coordination for Radial Distribution Networks Using Soft-Computing Techniques -- Priyavada and B. Kumar, A Review of Modified Particle Swarm Optimization Method -- D. Patel and S. Sharma Automated Detection of Elephant Using AI Techniques -- P. Rajak and P. Roy, Determination of Probability of Failure of Structures Using DBSCAN and Support Vector Machine -- Md. Nayer and S. C. Pandey, The Ensemble of Ant Colony Optimization and Gradient Descent Technique for Efficient Feature Selection and Data Classification -- A. K. Prasad and S. S. Thakur, s-Regularity Via Soft Ideal -- K. A. Pattani and S. Gautam, A Comprehensive Study on Mobile Malwares: Mobile Covert Channels - Threats and Security -- F. Nikbakhtsarvestani, Overview of Incorporating Nonlinear Functions into Recurrent Neural Network Models -- Sangita A. Jaju, Sudhir B. Jagtap, and R. Shinde, A Soft Computing Approach for Predicting and Categorising Learner’s Performance using Fuzzy Model -- T. Kaur Bhatia, A. Kumar, M.K. Sharma, and S.S. Appadoo, A Fuzzy Logic based Approach to Solve Interval Multiobjective Nonlinear Transportation Problem: Suggested Modifications -- O. Castillo and P. Melin, Interval Type-3 Fuzzy Decision Making in Material Surface Quality Control -- F. A. Khan, J. Ali, Fatimah N. Albishi, and F. Gursoy, A Generalized Nonlinear Quasi-Variational-Like Inclusion Problem Involving Fuzzy Mappings -- B. Kohli, Approximate Optimality Conditions via Convexifactors for Multiobjective Programming Problems -- F. Mirdamadi, H. Monfared, Mehdi Asadi, and H. Soleimani, A Remark on Discontinuity of Fixed Point on Partial Metric Spaces -- A. Priya, A. Kumari and M. Singh, Implementation of Fuzzy Logic in Home Appliances -- S. Banerjee, B. Guha, A. Ghosh, and G. Bandyopadhyay, Portfolio Structure of Debt Mutual Funds in Indian Market -- R. Om Gayathri and R. Hemavathy, Fixed Point Theorems for Digital Images using Path Length Metric -- R. P. Verma, N. Kumar, S. Kumar, and Sruthi S., Emergency Help for Road Accidents -- E. Arul, S. Akash Kumar, R. Ragul, and Yuvaanesh V.B., Music Classification Based on Lyrics and Audio by using Machine Learning -- S. Khalil and U. M. Modibbo, Multiobjective Optimization for Hospital Nurse Scheduling Problem -- Z. Ghouli, On the Performance of a Flow Energy Harvester using Time Delay -- A. Badran, Identification of Some Spatial Coefficients in Some Engineering Topics -- U. Bhagirathi and R. Ajoodha, Automatic Venue Allocation for Varying Class Sizes using Scoring and Heuristic Hill-climbing -- N. K. Sharma, S. Kumar, A. Rajpal, and N. Kumar, DWT and Quantization based Digital Watermarking Scheme using Kernel OS-ELM -- P. Muralikrishna, P. Hemavathi, and K. Palanivel, Stress Level Analysis Using Bipolar Picture Fuzzy Set -- Y. Kimura and K. Shindo, Asymptotic Behavior of Resolvents on Complete Geodesic Spaces with General Perturbation Functions.
Record Nr. UNINA-9910682551603321
Singapore : , : Springer Nature Singapore Pte Ltd, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Soft computing and optimization : SCOTA 2021, Ranchi, India, March 26-27 / / edited by Syeda Darakhshan Jabeen, Javid Ali, and Oscar Castillo
Soft computing and optimization : SCOTA 2021, Ranchi, India, March 26-27 / / edited by Syeda Darakhshan Jabeen, Javid Ali, and Oscar Castillo
Edizione [1st ed. 2022.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore Pte Ltd, , [2022]
Descrizione fisica 1 online resource (362 pages)
Disciplina 519.3
Collana Springer Proceedings in Mathematics & Statistics
Soggetto topico Mathematical optimization
Soft computing
Informàtica tova
Optimització matemàtica
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 981-19-6406-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto R. Kumari, A. Nigam and S. Pushkar, Classification of MRI images for detecting Alzheimer Disease using Convolutional Neural Network -- S. D. Godwal and S. S. Kanojia, Optimum over Current Relays Coordination for Radial Distribution Networks Using Soft-Computing Techniques -- Priyavada and B. Kumar, A Review of Modified Particle Swarm Optimization Method -- D. Patel and S. Sharma Automated Detection of Elephant Using AI Techniques -- P. Rajak and P. Roy, Determination of Probability of Failure of Structures Using DBSCAN and Support Vector Machine -- Md. Nayer and S. C. Pandey, The Ensemble of Ant Colony Optimization and Gradient Descent Technique for Efficient Feature Selection and Data Classification -- A. K. Prasad and S. S. Thakur, s-Regularity Via Soft Ideal -- K. A. Pattani and S. Gautam, A Comprehensive Study on Mobile Malwares: Mobile Covert Channels - Threats and Security -- F. Nikbakhtsarvestani, Overview of Incorporating Nonlinear Functions into Recurrent Neural Network Models -- Sangita A. Jaju, Sudhir B. Jagtap, and R. Shinde, A Soft Computing Approach for Predicting and Categorising Learner’s Performance using Fuzzy Model -- T. Kaur Bhatia, A. Kumar, M.K. Sharma, and S.S. Appadoo, A Fuzzy Logic based Approach to Solve Interval Multiobjective Nonlinear Transportation Problem: Suggested Modifications -- O. Castillo and P. Melin, Interval Type-3 Fuzzy Decision Making in Material Surface Quality Control -- F. A. Khan, J. Ali, Fatimah N. Albishi, and F. Gursoy, A Generalized Nonlinear Quasi-Variational-Like Inclusion Problem Involving Fuzzy Mappings -- B. Kohli, Approximate Optimality Conditions via Convexifactors for Multiobjective Programming Problems -- F. Mirdamadi, H. Monfared, Mehdi Asadi, and H. Soleimani, A Remark on Discontinuity of Fixed Point on Partial Metric Spaces -- A. Priya, A. Kumari and M. Singh, Implementation of Fuzzy Logic in Home Appliances -- S. Banerjee, B. Guha, A. Ghosh, and G. Bandyopadhyay, Portfolio Structure of Debt Mutual Funds in Indian Market -- R. Om Gayathri and R. Hemavathy, Fixed Point Theorems for Digital Images using Path Length Metric -- R. P. Verma, N. Kumar, S. Kumar, and Sruthi S., Emergency Help for Road Accidents -- E. Arul, S. Akash Kumar, R. Ragul, and Yuvaanesh V.B., Music Classification Based on Lyrics and Audio by using Machine Learning -- S. Khalil and U. M. Modibbo, Multiobjective Optimization for Hospital Nurse Scheduling Problem -- Z. Ghouli, On the Performance of a Flow Energy Harvester using Time Delay -- A. Badran, Identification of Some Spatial Coefficients in Some Engineering Topics -- U. Bhagirathi and R. Ajoodha, Automatic Venue Allocation for Varying Class Sizes using Scoring and Heuristic Hill-climbing -- N. K. Sharma, S. Kumar, A. Rajpal, and N. Kumar, DWT and Quantization based Digital Watermarking Scheme using Kernel OS-ELM -- P. Muralikrishna, P. Hemavathi, and K. Palanivel, Stress Level Analysis Using Bipolar Picture Fuzzy Set -- Y. Kimura and K. Shindo, Asymptotic Behavior of Resolvents on Complete Geodesic Spaces with General Perturbation Functions.
Record Nr. UNISA-996518463903316
Singapore : , : Springer Nature Singapore Pte Ltd, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui