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Biblioteca

MARC Lista (tabellare)
Approximation and Online Algorithms [[electronic resource] ] : 19th International Workshop, WAOA 2021, Lisbon, Portugal, September 6–10, 2021, Revised Selected Papers / / edited by Jochen Koenemann, Britta Peis
Approximation and Online Algorithms [[electronic resource] ] : 19th International Workshop, WAOA 2021, Lisbon, Portugal, September 6–10, 2021, Revised Selected Papers / / edited by Jochen Koenemann, Britta Peis
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (286 pages)
Disciplina 516.11
Collana Theoretical Computer Science and General Issues
Soggetto topico Algorithms
Mathematics - Data processing
Computational Mathematics and Numerical Analysis
Algorismes en línia
Optimització matemàtica
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-92702-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto How to Navigate Through Obstacles -- Approximation Algorithms for Vertex- Connectivity Augmentation on the Cycle -- Constant Factor Approximation for Tracking Paths and Fault Tolerant Feedback Vertex Set -- An Improved Approximation Bound for Minimum Weight Dominating Set on Graphs of Bounded Arboricity -- Tight Inapproximability of Minimum Maximal Matching on Bipartite Graphs -- On b-Matchings and b-Edge Dominating Sets: A 2-Approximation Algorithm for the 4-Edge Dominating Set Problem -- The Traveling k-Median Problem: Approximating Optimal Network Coverage -- EPTAS for Load Balancing Problem on Parallel Machines with a Non-renewable Resource -- Several methods of analysis for cardinality constrained bin packing -- Leah Epstein Weighted completion time minimization for capacitated parallel machines -- Server Cloud Scheduling -- FIFO and Randomized Competitive Packet Routing Games -- Improved Online Algorithm for Fractional Knapsack in the Random Order Model -- Improved Online Algorithm for Fractional Knapsack in the Random Order Model -- Improved Analysis of Online Balanced Clustering -- Precedence-Constrained Covering Problems with Multiplicity Constraints -- Contention Resolution, Matrix Scaling and Fair Allocation.
Record Nr. UNINA-9910520076503321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Approximation and Online Algorithms [[electronic resource] ] : 18th International Workshop, WAOA 2020, Virtual Event, September 9–10, 2020, Revised Selected Papers / / edited by Christos Kaklamanis, Asaf Levin
Approximation and Online Algorithms [[electronic resource] ] : 18th International Workshop, WAOA 2020, Virtual Event, September 9–10, 2020, Revised Selected Papers / / edited by Christos Kaklamanis, Asaf Levin
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (247 pages)
Disciplina 005.1
Collana Theoretical Computer Science and General Issues
Soggetto topico Algorithms
Computer science - Mathematics
Discrete mathematics
Data structures (Computer science)
Information theory
Computer science
Application software
Symbolic and Algebraic Manipulation
Discrete Mathematics in Computer Science
Data Structures and Information Theory
Theory of Computation
Computer and Information Systems Applications
Algorismes en línia
Optimització matemàtica
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-80879-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Design and analysis of algorithms -- Online algorithms, approximation algorithms analysis -- Algorithmic game theory and mechanism design -- Parameterized complexity -- Scheduling algorithms -- Competitive analysis. Packing and covering problems -- Rounding techniques.
Record Nr. UNISA-996464413103316
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Approximation and Online Algorithms [[electronic resource] ] : 19th International Workshop, WAOA 2021, Lisbon, Portugal, September 6–10, 2021, Revised Selected Papers / / edited by Jochen Koenemann, Britta Peis
Approximation and Online Algorithms [[electronic resource] ] : 19th International Workshop, WAOA 2021, Lisbon, Portugal, September 6–10, 2021, Revised Selected Papers / / edited by Jochen Koenemann, Britta Peis
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (286 pages)
Disciplina 516.11
Collana Theoretical Computer Science and General Issues
Soggetto topico Algorithms
Mathematics - Data processing
Computational Mathematics and Numerical Analysis
Algorismes en línia
Optimització matemàtica
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-92702-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto How to Navigate Through Obstacles -- Approximation Algorithms for Vertex- Connectivity Augmentation on the Cycle -- Constant Factor Approximation for Tracking Paths and Fault Tolerant Feedback Vertex Set -- An Improved Approximation Bound for Minimum Weight Dominating Set on Graphs of Bounded Arboricity -- Tight Inapproximability of Minimum Maximal Matching on Bipartite Graphs -- On b-Matchings and b-Edge Dominating Sets: A 2-Approximation Algorithm for the 4-Edge Dominating Set Problem -- The Traveling k-Median Problem: Approximating Optimal Network Coverage -- EPTAS for Load Balancing Problem on Parallel Machines with a Non-renewable Resource -- Several methods of analysis for cardinality constrained bin packing -- Leah Epstein Weighted completion time minimization for capacitated parallel machines -- Server Cloud Scheduling -- FIFO and Randomized Competitive Packet Routing Games -- Improved Online Algorithm for Fractional Knapsack in the Random Order Model -- Improved Online Algorithm for Fractional Knapsack in the Random Order Model -- Improved Analysis of Online Balanced Clustering -- Precedence-Constrained Covering Problems with Multiplicity Constraints -- Contention Resolution, Matrix Scaling and Fair Allocation.
Record Nr. UNISA-996464427803316
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Approximation and Online Algorithms [[electronic resource] ] : 18th International Workshop, WAOA 2020, Virtual Event, September 9–10, 2020, Revised Selected Papers / / edited by Christos Kaklamanis, Asaf Levin
Approximation and Online Algorithms [[electronic resource] ] : 18th International Workshop, WAOA 2020, Virtual Event, September 9–10, 2020, Revised Selected Papers / / edited by Christos Kaklamanis, Asaf Levin
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (247 pages)
Disciplina 005.1
Collana Theoretical Computer Science and General Issues
Soggetto topico Algorithms
Computer science - Mathematics
Discrete mathematics
Data structures (Computer science)
Information theory
Computer science
Application software
Symbolic and Algebraic Manipulation
Discrete Mathematics in Computer Science
Data Structures and Information Theory
Theory of Computation
Computer and Information Systems Applications
Algorismes en línia
Optimització matemàtica
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-80879-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Design and analysis of algorithms -- Online algorithms, approximation algorithms analysis -- Algorithmic game theory and mechanism design -- Parameterized complexity -- Scheduling algorithms -- Competitive analysis. Packing and covering problems -- Rounding techniques.
Record Nr. UNINA-9910490024503321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Assembly line balancing under uncertain task time and demand volatility / / Yuchen Li
Assembly line balancing under uncertain task time and demand volatility / / Yuchen Li
Autore Li Yuchen
Pubbl/distr/stampa Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (164 pages)
Disciplina 670.427
Collana Engineering applications of computational methods
Soggetto topico Assembly-line balancing
Models matemàtics
Optimització matemàtica
Enginyeria de producció
Soggetto genere / forma Llibres electrònics
ISBN 9789811942150
9789811942143
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Foreword -- Acknowledgement -- Contents -- 1 Introduction -- 1.1 Research Background -- 1.2 Assembly Line Balancing Problem: Modeling -- 1.2.1 ALBP-F -- 1.2.2 ALBP-1 -- 1.2.3 ALBP-2 -- 1.2.4 ALBP-E -- 1.3 Assembly Line Balancing Problem: Algorithms -- 1.3.1 Lower Bounds -- 1.3.2 Dominance Rules -- 1.3.3 Exact Solution Methods -- 1.3.4 Heuristic Methods -- 1.4 Summary of This Section -- References -- 2 Assembly Line Rebalancing Under Task Time Disruptions -- 2.1 Problem Description and Literature Review -- 2.2 Total Cost of the System -- 2.3 Rebalancing Policies -- 2.3.1 Continuous Rebalancing Policy -- 2.3.2 ``No rebalancing'' Policy -- 2.3.3 Periodic Rebalancing Policy -- 2.3.4 Data-Driven Rebalancing Policy -- 2.4 Numerical Experiments -- 2.4.1 Data -- 2.4.2 Analytical and Simulated Results -- 2.4.3 Comparison Between the ``no rebalancing'' Policy and Periodic Rebalancing -- 2.4.4 The Frequency of Rebalancing the Assembly Line -- 2.4.5 Periodic Versus Data-Driven Policies -- 2.4.6 The Effect of the Weights upper C 1C1 and upper C 2C2 on the Total Cost -- 2.4.7 Discussions -- 2.5 Summary of This Section -- 2.6 Appendix -- References -- 3 An Uncertain Programming Model for Two-Sided Assembly Line Balancing Under Uncertain Task Time -- 3.1 Two-Sided Assembly Line Balancing Problem -- 3.2 Uncertain Task Times and Uncertainty Theory -- 3.3 Discrepancies -- 3.4 An Uncertain Programming Model -- 3.4.1 Problem Setup -- 3.4.2 Mathematical Formulation -- 3.4.3 Feasibility -- 3.5 Solution Procedure -- 3.5.1 The Starting and Finishing Time of a Task -- 3.5.2 The Lower Bounds -- 3.5.3 Task Dominance -- 3.5.4 The General Framework of Simulated Annealing -- 3.5.5 The Solution Encoding and Initial Solution Generation -- 3.5.6 Fitness Function -- 3.5.7 Generation of a Feasible Task Assignment -- 3.5.8 Neighborhood Generation with Restart Mechanism.
3.5.9 Repair Algorithm -- 3.6 Computational Tests -- 3.7 Summary of This Section -- 3.8 Appendix -- References -- 4 System Reliability Optimization Under Uncertain Random Environment -- 4.1 Introduction -- 4.2 Literature Review -- 4.2.1 Assembly Line Balancing Under Non-stationary Task Time -- 4.2.2 Reliability Optimization and Belief Reliability Metric -- 4.2.3 Notations -- 4.3 Problem Formulation -- 4.3.1 Assumptions -- 4.3.2 Belief Reliability of ALBP-UR -- 4.3.3 Mathematical Formulation -- 4.4 Solution Methods -- 4.4.1 Encoding and Decoding -- 4.4.2 Neighborhood Generation and Pareto-Optimal Set Update -- 4.4.3 Restart Mechanism -- 4.5 Numerical Studies and Managerial Implications -- 4.5.1 Evaluation Methods -- 4.5.2 Experiments -- 4.5.3 Managerial Insights -- 4.6 The Summary of This Section -- 4.7 Appendix -- References -- 5 ALBP Under Learning Effect and Uncertain Demand -- 5.1 Introduction -- 5.2 Literature Review -- 5.2.1 The Mixed-Model Assembly Line Balancing Problem -- 5.2.2 Uncertain Demand and Learning Curve -- 5.2.3 Some Insights -- 5.3 Problem Formulation -- 5.3.1 Mathematical Formulation -- 5.4 Solution Procedure -- 5.4.1 Mixed-Integer Programming-Based Heuristic -- 5.4.2 Variable Neighborhood Search -- 5.4.3 Encoding and Decoding -- 5.4.4 Neighborhood Generation -- 5.4.5 Restart Mechanism -- 5.5 Numerical Experiments -- 5.5.1 Data -- 5.5.2 Algorithmic Comparisons on the Weighted Sum of Objectives -- 5.5.3 Algorithmic Comparisons on the Pareto Objectives -- 5.5.4 Discussions -- 5.6 The Summary of This Section -- References -- 6 A Joint Optimization of ALBP and Lot-Sizing Under Demand Uncertainty -- 6.1 Introduction -- 6.2 Literature Review -- 6.2.1 The Multi-item Assembly Line Balancing Problem -- 6.2.2 Multi-item Capacitated Lot-Sizing Problem -- 6.2.3 Risk-Averse Two-Stage Stochastic Programming and Conditional Value-at-Risk.
6.2.4 Facial Mask Production -- 6.2.5 Insights from the Literature -- 6.3 Problem Formulation -- 6.3.1 The Two-Stage Stochastic Problem -- 6.3.2 The Multi-item Assembly Line Balancing Problem -- 6.3.3 An Optimization Model for the MCALB-LS-UD -- 6.4 Solution Methods -- 6.4.1 Reformulation -- 6.4.2 Valid Inequalities -- 6.5 A Case Study -- 6.6 Computational Experiments -- 6.7 Summary of This Section -- 6.8 Appendix -- 6.8.1 Acronyms -- 6.8.2 Definitions and Results -- 6.8.3 Data for the Illustrative Example -- 6.8.4 Data for the Case Study -- References -- 7 The Summary of the Book.
Record Nr. UNINA-9910592992703321
Li Yuchen  
Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Assembly line balancing under uncertain task time and demand volatility / / Yuchen Li
Assembly line balancing under uncertain task time and demand volatility / / Yuchen Li
Autore Li Yuchen
Pubbl/distr/stampa Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (164 pages)
Disciplina 670.427
Collana Engineering applications of computational methods
Soggetto topico Assembly-line balancing
Models matemàtics
Optimització matemàtica
Enginyeria de producció
Soggetto genere / forma Llibres electrònics
ISBN 9789811942150
9789811942143
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Foreword -- Acknowledgement -- Contents -- 1 Introduction -- 1.1 Research Background -- 1.2 Assembly Line Balancing Problem: Modeling -- 1.2.1 ALBP-F -- 1.2.2 ALBP-1 -- 1.2.3 ALBP-2 -- 1.2.4 ALBP-E -- 1.3 Assembly Line Balancing Problem: Algorithms -- 1.3.1 Lower Bounds -- 1.3.2 Dominance Rules -- 1.3.3 Exact Solution Methods -- 1.3.4 Heuristic Methods -- 1.4 Summary of This Section -- References -- 2 Assembly Line Rebalancing Under Task Time Disruptions -- 2.1 Problem Description and Literature Review -- 2.2 Total Cost of the System -- 2.3 Rebalancing Policies -- 2.3.1 Continuous Rebalancing Policy -- 2.3.2 ``No rebalancing'' Policy -- 2.3.3 Periodic Rebalancing Policy -- 2.3.4 Data-Driven Rebalancing Policy -- 2.4 Numerical Experiments -- 2.4.1 Data -- 2.4.2 Analytical and Simulated Results -- 2.4.3 Comparison Between the ``no rebalancing'' Policy and Periodic Rebalancing -- 2.4.4 The Frequency of Rebalancing the Assembly Line -- 2.4.5 Periodic Versus Data-Driven Policies -- 2.4.6 The Effect of the Weights upper C 1C1 and upper C 2C2 on the Total Cost -- 2.4.7 Discussions -- 2.5 Summary of This Section -- 2.6 Appendix -- References -- 3 An Uncertain Programming Model for Two-Sided Assembly Line Balancing Under Uncertain Task Time -- 3.1 Two-Sided Assembly Line Balancing Problem -- 3.2 Uncertain Task Times and Uncertainty Theory -- 3.3 Discrepancies -- 3.4 An Uncertain Programming Model -- 3.4.1 Problem Setup -- 3.4.2 Mathematical Formulation -- 3.4.3 Feasibility -- 3.5 Solution Procedure -- 3.5.1 The Starting and Finishing Time of a Task -- 3.5.2 The Lower Bounds -- 3.5.3 Task Dominance -- 3.5.4 The General Framework of Simulated Annealing -- 3.5.5 The Solution Encoding and Initial Solution Generation -- 3.5.6 Fitness Function -- 3.5.7 Generation of a Feasible Task Assignment -- 3.5.8 Neighborhood Generation with Restart Mechanism.
3.5.9 Repair Algorithm -- 3.6 Computational Tests -- 3.7 Summary of This Section -- 3.8 Appendix -- References -- 4 System Reliability Optimization Under Uncertain Random Environment -- 4.1 Introduction -- 4.2 Literature Review -- 4.2.1 Assembly Line Balancing Under Non-stationary Task Time -- 4.2.2 Reliability Optimization and Belief Reliability Metric -- 4.2.3 Notations -- 4.3 Problem Formulation -- 4.3.1 Assumptions -- 4.3.2 Belief Reliability of ALBP-UR -- 4.3.3 Mathematical Formulation -- 4.4 Solution Methods -- 4.4.1 Encoding and Decoding -- 4.4.2 Neighborhood Generation and Pareto-Optimal Set Update -- 4.4.3 Restart Mechanism -- 4.5 Numerical Studies and Managerial Implications -- 4.5.1 Evaluation Methods -- 4.5.2 Experiments -- 4.5.3 Managerial Insights -- 4.6 The Summary of This Section -- 4.7 Appendix -- References -- 5 ALBP Under Learning Effect and Uncertain Demand -- 5.1 Introduction -- 5.2 Literature Review -- 5.2.1 The Mixed-Model Assembly Line Balancing Problem -- 5.2.2 Uncertain Demand and Learning Curve -- 5.2.3 Some Insights -- 5.3 Problem Formulation -- 5.3.1 Mathematical Formulation -- 5.4 Solution Procedure -- 5.4.1 Mixed-Integer Programming-Based Heuristic -- 5.4.2 Variable Neighborhood Search -- 5.4.3 Encoding and Decoding -- 5.4.4 Neighborhood Generation -- 5.4.5 Restart Mechanism -- 5.5 Numerical Experiments -- 5.5.1 Data -- 5.5.2 Algorithmic Comparisons on the Weighted Sum of Objectives -- 5.5.3 Algorithmic Comparisons on the Pareto Objectives -- 5.5.4 Discussions -- 5.6 The Summary of This Section -- References -- 6 A Joint Optimization of ALBP and Lot-Sizing Under Demand Uncertainty -- 6.1 Introduction -- 6.2 Literature Review -- 6.2.1 The Multi-item Assembly Line Balancing Problem -- 6.2.2 Multi-item Capacitated Lot-Sizing Problem -- 6.2.3 Risk-Averse Two-Stage Stochastic Programming and Conditional Value-at-Risk.
6.2.4 Facial Mask Production -- 6.2.5 Insights from the Literature -- 6.3 Problem Formulation -- 6.3.1 The Two-Stage Stochastic Problem -- 6.3.2 The Multi-item Assembly Line Balancing Problem -- 6.3.3 An Optimization Model for the MCALB-LS-UD -- 6.4 Solution Methods -- 6.4.1 Reformulation -- 6.4.2 Valid Inequalities -- 6.5 A Case Study -- 6.6 Computational Experiments -- 6.7 Summary of This Section -- 6.8 Appendix -- 6.8.1 Acronyms -- 6.8.2 Definitions and Results -- 6.8.3 Data for the Illustrative Example -- 6.8.4 Data for the Case Study -- References -- 7 The Summary of the Book.
Record Nr. UNISA-996490346503316
Li Yuchen  
Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Bayesian and high-dimensional global optimization / / Anatoly Zhigljavsky, Antanas Žilinskas
Bayesian and high-dimensional global optimization / / Anatoly Zhigljavsky, Antanas Žilinskas
Autore Zhigli͡avskiĭ A. A (Anatoliĭ Aleksandrovich)
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (VIII, 118 p. 54 illus., 38 illus. in color.)
Disciplina 519.7
Collana SpringerBriefs in Optimization
Soggetto topico Nonconvex programming
Optimització matemàtica
Estadística bayesiana
Soggetto genere / forma Llibres electrònics
ISBN 3-030-64712-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1 Space-filling in high-dimensional sets -- 2 Bi-objective decisions and partition based methods in Bayesian global optimization -- 3 Global random search in high dimensions.
Record Nr. UNINA-9910483396703321
Zhigli͡avskiĭ A. A (Anatoliĭ Aleksandrovich)  
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Bayesian and high-dimensional global optimization / / Anatoly Zhigljavsky, Antanas Žilinskas
Bayesian and high-dimensional global optimization / / Anatoly Zhigljavsky, Antanas Žilinskas
Autore Zhigli͡avskiĭ A. A (Anatoliĭ Aleksandrovich)
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (VIII, 118 p. 54 illus., 38 illus. in color.)
Disciplina 519.7
Collana SpringerBriefs in Optimization
Soggetto topico Nonconvex programming
Optimització matemàtica
Estadística bayesiana
Soggetto genere / forma Llibres electrònics
ISBN 3-030-64712-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1 Space-filling in high-dimensional sets -- 2 Bi-objective decisions and partition based methods in Bayesian global optimization -- 3 Global random search in high dimensions.
Record Nr. UNISA-996466545003316
Zhigli͡avskiĭ A. A (Anatoliĭ Aleksandrovich)  
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Black box optimization, machine learning, and no-free lunch theorems / / Panos M. Pardalos, Varvara Rasskazova, Michael N. Vrahatis, editors
Black box optimization, machine learning, and no-free lunch theorems / / Panos M. Pardalos, Varvara Rasskazova, Michael N. Vrahatis, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (393 pages)
Disciplina 006.31
Collana Springer Optimization and Its Applications
Soggetto topico Machine learning - Mathematics
Aprenentatge automàtic
Optimització matemàtica
Algorismes computacionals
Soggetto genere / forma Llibres electrònics
ISBN 3-030-66515-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Learning Enabled Constrained Black-Box Optimization -- 1 Introduction -- 2 Constrained Black-Box Optimization -- 3 The Basic Probabilistic Framework -- 3.1 Gaussian Processes -- 3.2 GP-Based Optimization -- 4 Constrained Bayesian Optimization -- 5 Constrained Bayesian Optimization for Partially Defined Objective Functions -- 6 Software for the Generation of Constrained Test Problems -- 6.1 Emmental-Type GKLS Generator -- 7 Conclusions -- References -- Black-Box Optimization: Methods and Applications -- 1 Introduction -- 2 Overview of BBO Methods -- 2.1 Direct Search Methods -- 2.1.1 Simplex Search -- 2.1.2 Coordinate Search -- 2.1.3 Generalized Pattern Search -- 2.1.4 Mesh Adaptive Direct Search -- 2.2 Model-Based Methods -- 2.2.1 Model-Based Trust Region -- 2.2.2 Projection-Based Methods -- 2.3 Heuristic Methods -- 2.3.1 DIRECT -- 2.3.2 Multilevel Coordinate Search -- 2.3.3 Hit-and-Run algorithms -- 2.3.4 Simulated Annealing -- 2.3.5 Genetic Algorithm -- 2.3.6 Particle Swarm Optimization -- 2.3.7 Surrogate Management Framework -- 2.3.8 Branch and Fit -- 2.4 Hybrid Methods -- 2.5 Extension to Constrained Problems -- 2.5.1 Penalty Method -- 2.5.2 Augmented Lagrangian -- 2.5.3 Filter Method -- 2.5.4 Surrogate Modeling -- 3 BBO Solvers -- 4 Recent Applications -- 4.1 Automatic Machine Learning -- 4.2 Optimization Solvers -- 4.3 Fluid Mechanics -- 4.4 Oilfield Development and Operations -- 4.5 Chemical and Biochemical Engineering -- 5 Open Problems and Future Research Directions -- References -- Tuning Algorithms for Stochastic Black-Box Optimization: State of the Art and Future Perspectives -- 1 Introduction -- 2 Tuning: Strategies -- 2.1 Key Topics -- 2.2 Stochastic Optimization Algorithms -- 2.3 Algorithm Tuning -- 2.4 Example: Grefenstette's Study of Control Parameters for Genetic Algorithms.
2.5 No Free Lunch Theorems -- 2.6 Tuning for Deterministic Algorithms -- 3 Test Sets -- 3.1 Test Functions -- 3.2 Application Domains -- 3.2.1 Tuning in Industry -- 3.2.2 Energy -- 3.2.3 Water Industry -- 3.2.4 Steel Industry -- 3.2.5 Automotive -- 3.2.6 Information Technology -- 4 Statistical Considerations -- 4.1 Experimental Setup -- 4.2 Design of Experiments -- 4.3 Measuring Performance -- 4.4 Reporting Results -- 5 Parallelization -- 5.1 Overview -- 5.2 Simplistic Approaches -- 5.3 Parallelization in Surrogate Model-Based Optimization -- 5.3.1 Uncertainty-Based Methods -- 5.3.2 Surrogate-Assisted Algorithms -- 6 Tuning Approaches -- 6.1 Overview -- 6.2 Manual Tuning -- 6.3 Automatic Tuning -- 6.4 Interactive Tuning -- 6.5 Internal Tuning -- 7 Tuning Software -- 7.1 Overview -- 7.2 IRACE -- 7.3 SPOT -- 7.4 SMAC -- 7.5 ParamILS -- 7.6 GGA -- 7.7 Usability and Availability of Tuning Software -- 7.8 Example: SPOT -- 8 Research Directions and Open Problems -- 9 Summary and Outlook -- References -- Quality-Diversity Optimization: A Novel Branch of Stochastic Optimization -- 1 Introduction -- 2 Problem Formulation -- 2.1 Collections of Solutions -- 2.2 How Do We Measure the Performance of a QD Algorithm? -- 3 Optimizing a Collection of Solutions -- 3.1 MAP-Elites -- 3.2 A Unified Framework -- 3.2.1 Containers -- 3.2.2 Selection Operators -- 3.2.3 Population Scores -- 3.3 Considerations of Quality-Diversity Optimization -- 4 Origins and Related Work -- 4.1 Searching for Diverse Behaviors -- 4.2 Connections to Multimodal Optimization -- 4.3 Connections to Multitask Optimization -- 5 Current Topics -- 5.1 Expensive Objective Functions -- 5.2 High-Dimensional Feature Space -- 5.3 Learning the Behavior Descriptor -- 5.4 Improving Variation Operators -- 5.5 Noisy Functions -- 6 Conclusion -- References.
Multi-Objective Evolutionary Algorithms: Past, Present, and Future -- 1 Introduction -- 2 Basic Concepts -- 3 The Past -- 3.1 Non-Elitist Non-Pareto Approaches -- 3.1.1 Linear Aggregating Functions -- 3.1.2 Vector Evaluated Genetic Algorithm (VEGA) -- 3.1.3 Lexicographic Ordering -- 3.1.4 Target-Vector Approaches -- 3.2 Non-Elitist Pareto-Based Approaches -- 3.2.1 Multi-Objective Genetic Algorithm (MOGA) -- 3.2.2 Nondominated Sorting Genetic Algorithm (NSGA) -- 3.2.3 Niched-Pareto Genetic Algorithm (NPGA) -- 3.3 Elitist Pareto-Based Approaches -- 3.3.1 The Strength Pareto Evolutionary Algorithm (SPEA) -- 3.3.2 The Pareto Archived Evolution Strategy (PAES) -- 3.3.3 The Nondominated Sorting Genetic Algorithm-II (NSGA-II) -- 4 The Present -- 4.1 Some Applications -- 5 The Future -- 6 Conclusions -- References -- Black-Box and Data-Driven Computation -- 1 Introduction -- 2 Black Box and Oracle -- 3 Reduction -- 4 Data-Driven Computation -- References -- Mathematically Rigorous Global Optimization and FuzzyOptimization -- 1 Introduction -- 2 Interval Analysis: Fundamentals and Philosophy -- 2.1 Overview -- 2.2 Interval Logic -- 2.3 Extensions -- 2.4 History and References -- 3 Fuzzy Sets: Fundamentals and Philosophy -- 3.1 Fuzzy Logic -- 3.2 A Brief History -- 4 The Branch and Bound Framework: Some Definitions and Details -- 5 Interval Technology: Some Details -- 5.1 Interval Newton Methods -- 5.2 Constraint Propagation -- 5.3 Relaxations -- 5.4 Interval Arithmetic Software -- 6 Fuzzy Technology: A Few Details -- 7 Conclusions -- References -- Optimization Under Uncertainty Explains Empirical Success of Deep Learning Heuristics -- 1 Formulation of the Problem -- 2 Why Rectified Linear Neurons Are Efficient: A Theoretical Explanation -- 3 Why Sigmoid Activation Functions -- 4 Selection of Poolings -- 5 Why Softmax -- 6 Which Averaging Should We Choose.
7 Proofs -- References -- Variable Neighborhood Programming as a Tool of Machine Learning -- 1 Introduction -- 2 Variable Neighborhood Search -- 3 Variable Neighborhood Programming -- 3.1 Solution Presentation -- 3.2 Neighborhood Structures -- 3.3 Elementary Tree Transformation in Automatic Programming -- 3.3.1 ETT in the Tree of an Undirected Graph -- 3.3.2 ETT in AP Tree -- 3.3.3 Bound on Cardinality of AP-ETT(T) -- 4 VNP for Symbolic Regression -- 4.1 Test Instances and Parameter Values -- 4.2 Comparison of BVNP with Other Methods -- 5 Life Expectancy Estimation as a Symbolic Regression Problem Solved by VNP: Case Study on Russian Districts -- 5.1 Life Expectancy Estimation as a Machine Learning Problem -- 5.2 VNP for Estimating Life Expectancy Problem -- 5.3 Case Study at Russian Districts -- 5.3.1 One-Attribute Analysis -- 5.3.2 Results and Discussion on 3-Attribute Data -- 5.4 Conclusions -- 6 Preventive Maintenance in Railway Planning as a Machine Learning Problem -- 6.1 Literature Review and Motivation -- 6.2 Reduced VNP for Solving the Preventive Maintenance Planning of Railway Infrastructure -- 6.2.1 Learning for Stage 1: Prediction -- 6.2.2 Learning for Stage 2: Classification -- 6.3 Computation Results -- 6.3.1 Prediction -- 6.3.2 Classification -- 6.4 Conclusions and Future Work -- 7 Conclusions -- References -- Non-lattice Covering and Quantization of High Dimensional Sets -- 1 Introduction -- 2 Weak Covering -- 2.1 Comparison of Designs from the View Point of Weak Covering -- 2.2 Reduction to the Probability of Covering a Point by One Ball -- 2.3 Designs of Theoretical Interest -- 3 Approximation of Cd(Zn,r) for Design 1 -- 3.1 Normal Approximation for PU,δ,α,r -- 3.2 Refined Approximation for PU,δ,α,r -- 3.3 Approximation for Cd(Zn,r) for Design 1 -- 4 Approximating Cd(Zn,r) for Design 2a -- 4.1 Normal Approximation for PU,δ,0,r.
4.2 Refined Approximation for PU,δ,0,r -- 4.3 Approximation for Cd(Zn,r) -- 5 Approximating Cd(Zn,r) for Design 2b -- 5.1 Establishing a Connection Between Sampling with and Without Replacement: General Case -- 5.2 Approximation of Cd(Zn,r) for Design 2b. -- 6 Numerical Study -- 6.1 Assessing Accuracy of Approximations of Cd(Zn,r) and Studying Their Dependence on δ -- 6.2 Comparison Across α -- 7 Quantization in a Cube -- 7.1 Quantization Error and Its Relation to Weak Covering -- 7.2 Quantization Error for Design 1 -- 7.3 Quantization Error for Design 2a -- 7.4 Quantization Error for Design 2b -- 7.5 Accuracy of Approximations for Quantization Error and the δ-Effect -- 8 Comparative Numerical Studies of Covering Properties for Several Designs -- 8.1 Covering Comparisons -- 8.2 Quantization Comparisons -- 9 Covering and Quantization in the d-Simplex -- 9.1 Characteristics of Interest -- 9.2 Numerical Investigation of the δ-Effect for d-Simplex -- 10 Appendix: An Auxiliary Lemma -- References -- Finding Effective SAT Partitionings Via Black-Box Optimization -- 1 Introduction -- 2 Preliminaries -- 2.1 Boolean Satisfiability Problem (SAT) -- 2.2 SAT-Based Cryptanalysis -- 3 Decomposition Sets and Backdoors in SAT with Application to Inversion of Discrete Functions -- 3.1 On Interconnection Between Plain Partitionings and Cryptographic Attacks -- 3.2 Using Monte Carlo Method to Estimate Runtime of SAT-Based Guess-and-Determine Attacks -- 4 Practical Aspects of Evaluating Effectiveness of SAT Partitionings -- 4.1 Narrowing Search Space to SUPBS -- 4.2 Applications of Incremental SAT Solving -- 4.3 Finding Partitionings via Incremental SAT -- 5 Employed Optimization Algorithms -- 6 Experimental Results -- 6.1 Considered Problems -- 6.2 Implementations of Objective Functions -- 6.3 Finding Effective SAT Partitionings.
6.4 Solving Hard SAT Instances via Found Partitionings.
Record Nr. UNISA-996466410203316
Cham, Switzerland : , : Springer, , [2021]
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Black box optimization, machine learning, and no-free lunch theorems / / Panos M. Pardalos, Varvara Rasskazova, Michael N. Vrahatis, editors
Black box optimization, machine learning, and no-free lunch theorems / / Panos M. Pardalos, Varvara Rasskazova, Michael N. Vrahatis, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (393 pages)
Disciplina 006.31
Collana Springer Optimization and Its Applications
Soggetto topico Machine learning - Mathematics
Aprenentatge automàtic
Optimització matemàtica
Algorismes computacionals
Soggetto genere / forma Llibres electrònics
ISBN 3-030-66515-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Learning Enabled Constrained Black-Box Optimization -- 1 Introduction -- 2 Constrained Black-Box Optimization -- 3 The Basic Probabilistic Framework -- 3.1 Gaussian Processes -- 3.2 GP-Based Optimization -- 4 Constrained Bayesian Optimization -- 5 Constrained Bayesian Optimization for Partially Defined Objective Functions -- 6 Software for the Generation of Constrained Test Problems -- 6.1 Emmental-Type GKLS Generator -- 7 Conclusions -- References -- Black-Box Optimization: Methods and Applications -- 1 Introduction -- 2 Overview of BBO Methods -- 2.1 Direct Search Methods -- 2.1.1 Simplex Search -- 2.1.2 Coordinate Search -- 2.1.3 Generalized Pattern Search -- 2.1.4 Mesh Adaptive Direct Search -- 2.2 Model-Based Methods -- 2.2.1 Model-Based Trust Region -- 2.2.2 Projection-Based Methods -- 2.3 Heuristic Methods -- 2.3.1 DIRECT -- 2.3.2 Multilevel Coordinate Search -- 2.3.3 Hit-and-Run algorithms -- 2.3.4 Simulated Annealing -- 2.3.5 Genetic Algorithm -- 2.3.6 Particle Swarm Optimization -- 2.3.7 Surrogate Management Framework -- 2.3.8 Branch and Fit -- 2.4 Hybrid Methods -- 2.5 Extension to Constrained Problems -- 2.5.1 Penalty Method -- 2.5.2 Augmented Lagrangian -- 2.5.3 Filter Method -- 2.5.4 Surrogate Modeling -- 3 BBO Solvers -- 4 Recent Applications -- 4.1 Automatic Machine Learning -- 4.2 Optimization Solvers -- 4.3 Fluid Mechanics -- 4.4 Oilfield Development and Operations -- 4.5 Chemical and Biochemical Engineering -- 5 Open Problems and Future Research Directions -- References -- Tuning Algorithms for Stochastic Black-Box Optimization: State of the Art and Future Perspectives -- 1 Introduction -- 2 Tuning: Strategies -- 2.1 Key Topics -- 2.2 Stochastic Optimization Algorithms -- 2.3 Algorithm Tuning -- 2.4 Example: Grefenstette's Study of Control Parameters for Genetic Algorithms.
2.5 No Free Lunch Theorems -- 2.6 Tuning for Deterministic Algorithms -- 3 Test Sets -- 3.1 Test Functions -- 3.2 Application Domains -- 3.2.1 Tuning in Industry -- 3.2.2 Energy -- 3.2.3 Water Industry -- 3.2.4 Steel Industry -- 3.2.5 Automotive -- 3.2.6 Information Technology -- 4 Statistical Considerations -- 4.1 Experimental Setup -- 4.2 Design of Experiments -- 4.3 Measuring Performance -- 4.4 Reporting Results -- 5 Parallelization -- 5.1 Overview -- 5.2 Simplistic Approaches -- 5.3 Parallelization in Surrogate Model-Based Optimization -- 5.3.1 Uncertainty-Based Methods -- 5.3.2 Surrogate-Assisted Algorithms -- 6 Tuning Approaches -- 6.1 Overview -- 6.2 Manual Tuning -- 6.3 Automatic Tuning -- 6.4 Interactive Tuning -- 6.5 Internal Tuning -- 7 Tuning Software -- 7.1 Overview -- 7.2 IRACE -- 7.3 SPOT -- 7.4 SMAC -- 7.5 ParamILS -- 7.6 GGA -- 7.7 Usability and Availability of Tuning Software -- 7.8 Example: SPOT -- 8 Research Directions and Open Problems -- 9 Summary and Outlook -- References -- Quality-Diversity Optimization: A Novel Branch of Stochastic Optimization -- 1 Introduction -- 2 Problem Formulation -- 2.1 Collections of Solutions -- 2.2 How Do We Measure the Performance of a QD Algorithm? -- 3 Optimizing a Collection of Solutions -- 3.1 MAP-Elites -- 3.2 A Unified Framework -- 3.2.1 Containers -- 3.2.2 Selection Operators -- 3.2.3 Population Scores -- 3.3 Considerations of Quality-Diversity Optimization -- 4 Origins and Related Work -- 4.1 Searching for Diverse Behaviors -- 4.2 Connections to Multimodal Optimization -- 4.3 Connections to Multitask Optimization -- 5 Current Topics -- 5.1 Expensive Objective Functions -- 5.2 High-Dimensional Feature Space -- 5.3 Learning the Behavior Descriptor -- 5.4 Improving Variation Operators -- 5.5 Noisy Functions -- 6 Conclusion -- References.
Multi-Objective Evolutionary Algorithms: Past, Present, and Future -- 1 Introduction -- 2 Basic Concepts -- 3 The Past -- 3.1 Non-Elitist Non-Pareto Approaches -- 3.1.1 Linear Aggregating Functions -- 3.1.2 Vector Evaluated Genetic Algorithm (VEGA) -- 3.1.3 Lexicographic Ordering -- 3.1.4 Target-Vector Approaches -- 3.2 Non-Elitist Pareto-Based Approaches -- 3.2.1 Multi-Objective Genetic Algorithm (MOGA) -- 3.2.2 Nondominated Sorting Genetic Algorithm (NSGA) -- 3.2.3 Niched-Pareto Genetic Algorithm (NPGA) -- 3.3 Elitist Pareto-Based Approaches -- 3.3.1 The Strength Pareto Evolutionary Algorithm (SPEA) -- 3.3.2 The Pareto Archived Evolution Strategy (PAES) -- 3.3.3 The Nondominated Sorting Genetic Algorithm-II (NSGA-II) -- 4 The Present -- 4.1 Some Applications -- 5 The Future -- 6 Conclusions -- References -- Black-Box and Data-Driven Computation -- 1 Introduction -- 2 Black Box and Oracle -- 3 Reduction -- 4 Data-Driven Computation -- References -- Mathematically Rigorous Global Optimization and FuzzyOptimization -- 1 Introduction -- 2 Interval Analysis: Fundamentals and Philosophy -- 2.1 Overview -- 2.2 Interval Logic -- 2.3 Extensions -- 2.4 History and References -- 3 Fuzzy Sets: Fundamentals and Philosophy -- 3.1 Fuzzy Logic -- 3.2 A Brief History -- 4 The Branch and Bound Framework: Some Definitions and Details -- 5 Interval Technology: Some Details -- 5.1 Interval Newton Methods -- 5.2 Constraint Propagation -- 5.3 Relaxations -- 5.4 Interval Arithmetic Software -- 6 Fuzzy Technology: A Few Details -- 7 Conclusions -- References -- Optimization Under Uncertainty Explains Empirical Success of Deep Learning Heuristics -- 1 Formulation of the Problem -- 2 Why Rectified Linear Neurons Are Efficient: A Theoretical Explanation -- 3 Why Sigmoid Activation Functions -- 4 Selection of Poolings -- 5 Why Softmax -- 6 Which Averaging Should We Choose.
7 Proofs -- References -- Variable Neighborhood Programming as a Tool of Machine Learning -- 1 Introduction -- 2 Variable Neighborhood Search -- 3 Variable Neighborhood Programming -- 3.1 Solution Presentation -- 3.2 Neighborhood Structures -- 3.3 Elementary Tree Transformation in Automatic Programming -- 3.3.1 ETT in the Tree of an Undirected Graph -- 3.3.2 ETT in AP Tree -- 3.3.3 Bound on Cardinality of AP-ETT(T) -- 4 VNP for Symbolic Regression -- 4.1 Test Instances and Parameter Values -- 4.2 Comparison of BVNP with Other Methods -- 5 Life Expectancy Estimation as a Symbolic Regression Problem Solved by VNP: Case Study on Russian Districts -- 5.1 Life Expectancy Estimation as a Machine Learning Problem -- 5.2 VNP for Estimating Life Expectancy Problem -- 5.3 Case Study at Russian Districts -- 5.3.1 One-Attribute Analysis -- 5.3.2 Results and Discussion on 3-Attribute Data -- 5.4 Conclusions -- 6 Preventive Maintenance in Railway Planning as a Machine Learning Problem -- 6.1 Literature Review and Motivation -- 6.2 Reduced VNP for Solving the Preventive Maintenance Planning of Railway Infrastructure -- 6.2.1 Learning for Stage 1: Prediction -- 6.2.2 Learning for Stage 2: Classification -- 6.3 Computation Results -- 6.3.1 Prediction -- 6.3.2 Classification -- 6.4 Conclusions and Future Work -- 7 Conclusions -- References -- Non-lattice Covering and Quantization of High Dimensional Sets -- 1 Introduction -- 2 Weak Covering -- 2.1 Comparison of Designs from the View Point of Weak Covering -- 2.2 Reduction to the Probability of Covering a Point by One Ball -- 2.3 Designs of Theoretical Interest -- 3 Approximation of Cd(Zn,r) for Design 1 -- 3.1 Normal Approximation for PU,δ,α,r -- 3.2 Refined Approximation for PU,δ,α,r -- 3.3 Approximation for Cd(Zn,r) for Design 1 -- 4 Approximating Cd(Zn,r) for Design 2a -- 4.1 Normal Approximation for PU,δ,0,r.
4.2 Refined Approximation for PU,δ,0,r -- 4.3 Approximation for Cd(Zn,r) -- 5 Approximating Cd(Zn,r) for Design 2b -- 5.1 Establishing a Connection Between Sampling with and Without Replacement: General Case -- 5.2 Approximation of Cd(Zn,r) for Design 2b. -- 6 Numerical Study -- 6.1 Assessing Accuracy of Approximations of Cd(Zn,r) and Studying Their Dependence on δ -- 6.2 Comparison Across α -- 7 Quantization in a Cube -- 7.1 Quantization Error and Its Relation to Weak Covering -- 7.2 Quantization Error for Design 1 -- 7.3 Quantization Error for Design 2a -- 7.4 Quantization Error for Design 2b -- 7.5 Accuracy of Approximations for Quantization Error and the δ-Effect -- 8 Comparative Numerical Studies of Covering Properties for Several Designs -- 8.1 Covering Comparisons -- 8.2 Quantization Comparisons -- 9 Covering and Quantization in the d-Simplex -- 9.1 Characteristics of Interest -- 9.2 Numerical Investigation of the δ-Effect for d-Simplex -- 10 Appendix: An Auxiliary Lemma -- References -- Finding Effective SAT Partitionings Via Black-Box Optimization -- 1 Introduction -- 2 Preliminaries -- 2.1 Boolean Satisfiability Problem (SAT) -- 2.2 SAT-Based Cryptanalysis -- 3 Decomposition Sets and Backdoors in SAT with Application to Inversion of Discrete Functions -- 3.1 On Interconnection Between Plain Partitionings and Cryptographic Attacks -- 3.2 Using Monte Carlo Method to Estimate Runtime of SAT-Based Guess-and-Determine Attacks -- 4 Practical Aspects of Evaluating Effectiveness of SAT Partitionings -- 4.1 Narrowing Search Space to SUPBS -- 4.2 Applications of Incremental SAT Solving -- 4.3 Finding Partitionings via Incremental SAT -- 5 Employed Optimization Algorithms -- 6 Experimental Results -- 6.1 Considered Problems -- 6.2 Implementations of Objective Functions -- 6.3 Finding Effective SAT Partitionings.
6.4 Solving Hard SAT Instances via Found Partitionings.
Record Nr. UNINA-9910483695503321
Cham, Switzerland : , : Springer, , [2021]
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