1  20
Next
 Singapore : Springer, 2023.
 Description
 Book — 1 online resource (viii, 246 pages) : illustrations (chiefly color).
 Summary

 Intro
 Preface
 Contents
 1 NatureInspired Algorithms in Optimization: Introduction, Hybridization, and Insights
 1 Introduction
 2 Optimization and Algorithms
 2.1 Components of Optimization
 2.2 Gradients and Optimization
 3 NatureInspired Algorithms
 3.1 Recent NatureInspired Algorithms
 3.2 Other Natureinspired Algorithms
 4 Hybridization
 4.1 Hybridization Schemes
 4.2 Issues and Warnings
 5 Insights and Recommendations
 References
 2 Ten New Benchmarks for Optimization
 1 Introduction
 2 Role of Benchmarks
 3 New Benchmark Functions
 3.1 Noisy Functions
 3.2 Nondifferentiable Functions
 3.3 Functions with Isolated Domains
 4 Benchmarks with Multiple Optimal Solutions
 4.1 Function on a Hyperboloid
 4.2 Nonsmooth Multilayered Functions
 5 Parameter Estimation as Benchmarks
 6 Integrals as Benchmarks
 7 Benchmarks of Infinite Dimensions
 7.1 Shortest Path Problem
 7.2 Shape Optimization
 8 Conclusions
 References
 3 Review of Parameter Tuning Methods for NatureInspired Algorithms
 1 Introduction
 2 Parameter Tuning
 2.1 Schematic Representation of Parameter Tuning
 2.2 Different Types of Optimality
 2.3 Approaches to Parameter Tuning
 3 Review of Parameter Tuning Methods
 3.1 Generic Methods for Parameter Tuning
 3.2 Online and Offline Tunings
 3.3 SelfParametrization and Fuzzy Methods
 3.4 Machine LearningBased Methods
 4 Discussions and Recommendations
 References
 4 QOPTLib: A Quantum Computing Oriented Benchmark for Combinatorial Optimization Problems
 1 Introduction
 2 Description of the Problems
 2.1 Traveling Salesman Problem
 2.2 Vehicle Routing Problem
 2.3 Bin Packing Problem
 2.4 Maximum Cut Problem
 3 Introducing the Generated QOPTLib Benchmarks
 4 Preliminary Experimentation
 5 Conclusions and Further Work
 References
 5 Benchmarking for Discrete Cuckoo Search: Three Case Studies
 1 Introduction
 2 COPs Statements
 2.1 Studied COPs
 2.2 Formal Definitions
 3 DCS Common Resolution
 3.1 General Algorithm
 3.2 Main Functions
 4 Studied Case Resolutions
 4.1 Solutions
 4.2 Moves
 5 Experimental Tests
 5.1 Parameters
 5.2 Instances
 5.3 Statistic Tests
 6 Conclusion
 References
 6 Metaheuristics for Feature Selection: A Comprehensive Comparison Using Opytimizer
 1 Introduction
 2 Literature Review
 3 Handson Opytimizer: A Python Implementation for Metaheuristic Optimization
 4 Case Study: Feature Selection
 4.1 Methodology
 4.2 Experiments
 5 Conclusions
 References
 7 AL4SLEO: An Active Learning Solution for the Semantic Labelling of Earth Observation Satellite ImagesPart 1
 1 Introduction
 2 State of the Art
 3 Data Set Description
 4 Active Learning
 5 Semantic Labelling
 6 Conclusions
 References
 Cham, Switzerland : Springer, [2023]
 Description
 Book — 1 online resource (xxxv, 422 pages) : illustrations
 Summary

 Preface and Book of Abstracts
 Chapter. 1. Optimal Layered Defense for Site Protection
 Chapter. 2. SARAHbased Variancereduced Algorithm for Stochastic Finitesum Cocoercive Variational Inequalities
 Chapter. 3. Dimensionality reduction using pseudoBoolean polynomials for cluster analysis
 Chapter. 4. PseudoBoolean polynomials approach to edge detection and image segmentation
 Chapter. 5. Purifying Data by Machine Learning with Certainty Levels
 Chapter. 6. On impact of data models on predictability assessment of time series
 Chapter. 7. A threestep method for audience extension in Internet advertising using an industrial taxonomy
 Chapter. 8. From Prebase in Automata Theory to Data Analysis: Boris Mirkin's Way
 Chapter. 9. Manipulability of aggregation procedures for the case of large numbers of voters
 Chapter. 10. Preferences over mixed manna
 Chapter. 11. About Some Clustering Algorithms in Evidence Theory
 Chapter. 12. Inferring Multiple Consensus Trees and Supertrees Using Clustering: A Review
 Chapter. 13. Anomaly Detection With Neural Network Using a Generator
 Chapter. 14. Controllability of triangular systems with phase space change
 Chapter. 15. A Parallel Linear Active Set Method
 Chapter. 16. Mean Values: A Multicriterial Analysis
 Chapter. 17. Data and Text Interpretation in Social Media: Urban Planning Conflicts
 Chapter. 18. Visual Explainable Machine Learning for HighStake DecisionMaking with Worst Case Estimates
 Chapter. 19. Algorithm of trading on the stock market, providing satisfactory results
 Chapter. 20. Classification using Marginalized Maximum Likelihood Estimation and BlackBox Variational Inference
 Chapter. 21. Generating Genomic Maps of ZDNA with the Transformer Algorithm
 Chapter. 22. Manipulation by Coalitions in Voting with Incomplete Information
 Chapter. 23. Rethinking Probabilistic Topic Modeling from the Point of View of Classical NonBayesian Regularization.
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 Du, Dingzhu.
 Cham : Springer, 2022.
 Description
 Book — 1 online resource (407 p.).
 Summary

 1. Introduction.
 2. DivideandConquer.
 3. Dynamic Programming and Shortest Path.
 4. Greedy Algorithm and Spanning Tree.
 5. Incremental Method and Maximum Network Flow.
 6. Linear Programming.
 7. PrimalDual Methods and Minimum Cost Flow.
 8. NPhard Problems and Approximation Algorithms.
 9. Restriction and Steiner Tree.
 10. Greedy Approximation and Submodular Optimization.
 11. Relaxation and Rounding.
 12. Nonsubmodular Optimization. Bibliography.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Cham : Springer, [2022]
 Description
 Book — 1 online resource : illustrations (some color).
 Summary

 A New Computational Paradigm Using GrossoneBased Numerical Infinities and Infinitesimals. Nonlinear Optimization: A Brief Overview. The role of grossone in Nonlinear Programming and Exact Penalty Methods.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Zhang, Weihong, author.
 Amsterdam, Netherlands ; Oxford, United Kingdom ; Cambridge, MA : Elsevier, [2021]
 Description
 Book — 1 online resource
 Summary

The FeatureDriven Method for Structural Optimization details a novel structural optimization method within a CAD framework, integrating structural optimization and featurebased design. The book presents cuttingedge research on advanced structures and introduces the featuredriven structural optimization method by regarding engineering features as basic design primitives. Consequently, it presents a method that allows structural optimization and feature design to be done simultaneously so that feature attributes are preserved throughout the design process. The book illustrates and supports the effectiveness of the method described, showing potential applications through numerical modeling techniques and programming. This volume presents a highperformance optimization method adapted to engineering structuresa novel perspective that will help engineers in the computation, modeling and design of advanced structures.
7. New optimization algorithms and its applications : atombased, ecosystembased and economicsbased [2021]
 Zhang, Zhenxing, Hydrologist, University of Illinois at UrbanaChampaign, USA. author.
 Amsterdam : Elsevier, 2021.
 Description
 Book — 1 online resource
 Summary

 1. Introduction
 2. Atom Search Optimization Algorithm
 3. Engineering Applications of Atom Search Optimization Algorithm
 4. Artificial Ecosystembased Optimization Algorithm
 5. Engineering Applications of Artificial EcosystemBased Optimization Algorithm
 6. SupplyDemandbased Optimization
 7. Engineering Applications of SupplyDemandbased Optimization Appendix A. Benchmark Functions B. Engineering Design Problems C. Codes in MATLAB.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Stefanov, Stefan M., author.
 Second edition.  Cham, Switzerland : Springer, 2021.
 Description
 Book — 1 online resource (xvii, 356 pages) : illustrations. Digital: text file; PDF.
 Summary

 Preface to the New Edition
 Preface.1 Preliminaries: Convex Analysis and Convex Programming
 Part I. Separable Programming
 2 Introduction: Approximating the Separable Problem
 3. Convex Separable Programming
 4. Separable Programming: A Dynamic Programming Approach
 Part II. Convex Separable Programming With Bounds on the Variables
 5. Statement of the Main Problem. Basic Result
 6. Version One: Linear Equality Constraints
 7. The Algorithms
 8. Version Two: Linear Constraint of the Form \geq
 9. WellPosedness of Optimization Problems. On the Stability of the Set of Saddle Points of the Lagrangian
 10. Extensions
 11. Applications and Computational Experiments
 Part III. Selected Supplementary Topics and Applications
 12. Applications of Convex Separable Unconstrained Nondifferentiable Optimization to Approximation Theory
 13. About Projections in the Implementation of Stochastic Quasigradient Methods to Some Probabilistic Inventory Control Problems
 14. Valid Inequalities, Cutting Planes and Integrality ofthe Knapsack Polytope
 15. Relaxation of the Equality Constrained Convex Continuous Knapsack Problem
 16. On the Solution of Multidimensional Convex Separable Continuous Knapsack Problem with Bounded Variables
 17. Characterization of the Optimal Solution of the Convex Generalized Nonlinear Transportation Problem
 Appendices
 A. Some Definitions and Theorems from Calculus
 B. Metric, Banach and Hilbert Spaces
 C. Existence of Solutions to Optimization Problems : A General Approach
 D. Best Approximation: Existence and Uniqueness
 E. On the Solvability of a Quadratic Optimization Problem with a Feasible Region Defined as a Minkowski Sum of a Compact Set and Finitely Generated Convex Closed Cone F. On the CauchySchwarz Inequality Approach for Solving a Quadratic Optimization Problem
 G. Theorems of the Alternative
 Bibliography
 List of Notation
 List of Statements
 Index.
 Zaslavski, Alexander J.
 Cham : Springer, 2020.
 Description
 Book — 1 online resource (364 pages) Digital: text file.PDF.
 Summary

 Preface.
 1. Introduction.
 2. Subgradient Projection Algorithm.
 3. The Mirror Descent Algorithm.
 4. Gradient Algorithm with a Smooth Objective Function.
 5. An Extension of the Gradient Algorithm.
 6. Continuous Subgradient Method.
 7. An optimization problems with a composite objective function.
 8. A zerosum game with twoplayers.
 9. PDAbased method for convex optimization. 10 Minimization of quasiconvex functions.
 11. Minimization of sharp weakly convex functions.
 12. A Projected Subgradient Method for Nonsmooth Problems. References. Index. .
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Wilfer, Oleg.
 Wiesbaden, Germany : Springer Spektrum, [2020]
 Description
 Book — 1 online resource
 Summary

 Lagrange Duality for MultiComposed Optimization Problems. Duality Results for Minmax Location Problems. Solving Minmax Location Problems via Epigraphical Projection. Numerical Experiments.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Cham : Springer, 2020.
 Description
 Book — 1 online resource
 Summary

 Introduction. Part I: General Methods. Part II: Structure Exploiting Methods. Part III: Methods for Special Problems. Part IV: Derivativefree Methods.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Bagirov, Adil M.
 Cham : Springer, 2020.
 Description
 Book — 1 online resource (xx, 336 pages) : illustrations (some color)
 Summary

 Introduction. Introduction to Clustering. Clustering Algorithms. Nonsmooth Optimization Models in Cluster Analysis. Nonsmooth Optimization. Optimization based Clustering Algorithms. Implementation and Numerical Results. Conclusion.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
13. Nonsmooth optimization and its applications [2019]
 Cham, Switzerland : Birkhäuser, [2019]
 Description
 Book — vii, 149 pages : illustrations ; 25 cm.
 Summary

 A collection of nonsmooth Riemannian optimization problems / P.A. Absil and S. Hosseini
 An approximate ADMM for solving linearly constrained nonsmooth optimization problems with two blocks of variables / Adil M. Bagirov, Sona Taheri, Fusheng Bai, and Zhiyou Wu
 Tangent and normal cones for lowrank matrices / Seyedehsomayeh Hosseini, D. Russell Luke, and André Uschmajew
 Subdifferential enlargements and continuity properties of the [vu]decomposition in convex optimization / Shuai Liu, Claudia Sagastizábal, and Mikhail Solodov
 Proximal mappings and Moreau envelopes of singlevariable convex piecewise cubic functions and multivariable gauge functions / C. Planiden and X. Wang
 Newtonlike dynamics associated to nonconvex optimization problems / Radu Ioan Boţ and Ernö Robert Csetnek.
(source: Nielsen Book Data)
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA297 .I5 V.170  Unknown 
 Cham : Springer, 2019.
 Description
 Book — 1 online resource
 Summary

 Part 1. Risk Based Optimization of Integrated Fabrication/Fulfillment Supply Chains (Nasim Nezamoddini, Faisal Aqlan, Amirhosein Gholami). EGF: A New MultiThread Implementation Algorithm for the Packing Problem inspired by Electromagnetic Fields and Gravitational Effects (Felix MartinezRios and Jose Antonio MarmolejoSaucedo). The Vector Optimization Method for Solving Integer Linear Programming Problems. Application for the Unit Commitment Problem in Electrical Power Production (Lenar Nizamov). An Outer Approximation Algorithm for Capacitated Disassembly Scheduling Problem with Parts Commonality and Random Demand (Kanglin Liu, MengWang, ZhiHai Zhang),  MultiTree Decomposition Methods for LargeScale Mixed Integer Nonlinear Optimization (Ivo Nowak, Pavlo Muts, and Eligius M.T. Hendrix). An Embarrassingly Parallel Method for LargeScale Stochastic Programs (Burhaneddin Sandikci and Osman Y. OEzaltin). Part 2. How to Effectively Train Large Scale Machines (Avan Samareh, Mahshid Salemi Parizi). A Graph Search Algorithm for Solving Large Scale Median Problems on Real Road Networks (Saeed Ghanbartehrania, J. David Porterb, Mahnoush Samadi Dinania). Solving Large Scale Optimization Problems in the Transportation Industry and Beyond through Column Generation (Yanqi Xu). Dynamic Energy Management (Nicholas Moehle, Enzo Busseti, Stephen Boyd, and Matt Wytock). An ApproximationBased Approach for ChanceConstrained Vehicle Routing and Air Traffic Control Problems (Lijian Chen). Algorithmic Mechanism Design for Collaboration in Largescale Transportation Networks (Minghui Lai and Xiaoqiang Cai). KantorovichRubinstein Distance Minimization: Application to Location Problems (Viktor Kuzmenko, Stan Uryasev).
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
15. SOC functions and their applications [2019]
 Chen, JeinShan, author.
 Singapore : Springer, [2019]
 Description
 Book — 1 online resource (x, 206 pages)
 Summary

 SOC Functions. SOCconvexity and SOCMonotonity. Algorithmic Applications. SOC Means and SOC Inequalities. Possible Extensions.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
16. Budget Optimization and Allocation [2018]
 Sahana, Sudip Kumar.
 Sharjah : Bentham Science Publishers, 2018.
 Description
 Book — 1 online resource (164 pages)
 Summary

 Intro; CONTENTS; FOREWORD; PREFACE; DEDICATION; SUMMARY; Introduction; 1
 .1. IMPORTANCE AND CHALLENGES OF BUDGET ALLOCATION IN NATIONAL AND GLOBAL ECONOMY; Military; Health Care; Education; 1
 .2. ADVANTAGES AND DISADVANTAGES OF BUDGET ALLOCATION; 1.2
 .1. Benefits of Budget Allocation; 1.2
 .2. Disadvantage of Budget Allocation; CONCLUDING REMARKS; CONSENT FOR PUBLICATION; CONFLICT OF INTEREST; ACKNOWLEDGEMENTS; REFERENCES; Literature Review; 2
 .1. TRADITIONAL BUDGET ALLOCATION TECHNIQUE; 2.1
 .1. Rank and Selection Technique; 2.1
 .2. Incremental Budgeting Technique.
 2.1
 .3. Zerobased Budgeting Technique2.1
 .4. Ordinary Least Squares Technique (OLST); 2.1
 .5. Twostage Least Squares Technique (2SLST); 2
 .2. LINEAR OPTIMIZATION; 2
 .3. NONLINEAR OPTIMIZATION; 2
 .4. METAHEURISTIC OPTIMIZATION; 2.4
 .1. Pareto Efficiency or Pareto Optimality; Marginal Conditions of Pareto Optimality; Pareto Efficiency in Social welfare; 2.4
 .2. Optimal Computing Budget Allocation (OCBA); 2.4
 .3. Genetic Algorithm (GA); 2
 .5. LITERATURE SURVEY; 2
 .6. PROBLEM STATEMENT; CONCLUDING REMARKS; CONSENT FOR PUBLICATION; CONFLICT OF INTEREST; ACKNOWLEDGEMENTS; REFERENCES; Research Methodology.
 3
 .1. BUDGET ALLOCATION SCHEME/MODEL3
 .2. BUDGET OPTIMIZATION TECHNIQUE; 3.2
 .1. Proposed Evolutionary Computing based Framework for Budget Allocation and Optimization; 3.2.1
 .1. Mathematical Finance; 3.2.1
 .2. Evolutionary Computing Approach; Pseudo code for the Crossover Process; 3
 .3. BUDGET ALLOCATION TECHNIQUE; CONCLUDING REMARKS; CONSENT FOR PUBLICATION; CONFLICT OF INTEREST; ACKNOWLEDGEMENTS; REFERENCES; Result and Discussion; 4
 .1. TEST CASE
 1: GROWTH RATE CALCULATION; 4
 .2. TEST CASE
 2: PERCENT GROWTH RATE; 4
 .3. TEST CASE
 3: MEAN AND STANDARD DEVIATION TECHNIQUE.
 4
 .4. OPTIMIZATION TECHNIQUE
 1: OCBA TECHNIQUE4
 .5. OPTIMIZATION TECHNIQUE
 2: EA TECHNIQUE; 4
 .6. OPTIMIZATION TECHNIQUE
 3: GA OPTIMIZATION; 4
 .7. BUDGET ALLOCATION TECHNIQUE; 4.7
 .1. Scheme
 1: National Council of Education Research and Training (NCERT); 4.7
 .2. Scheme
 2: Kendriya Vidyalaya Sangathan (KVS); 4.7
 .3. Scheme
 3: Central Tibetan School Society Administration; 4.7
 .4. Scheme
 4: Scheme for Setting Up 6000 Model Schools; 4.7
 .5. Scheme
 5: Rashtriya Madhyamik Shiksha Abhiyan (RMSA); 4.7
 .6. Scheme
 6: Navodaya Vidyalaya Samiti (NVS); 4
 .8. OUTPUT OF BUDGET ALLOCATION; CONCLUDING REMARKS.
 CONSENT FOR PUBLICATIONCONFLICT OF INTEREST; ACKNOWLEDGEMENTS; REFERENCES; APPENDIX A; ALLOCATION OCBA; Public Class Allocation_OCBA; Applet Window Code for Simulation; Simulation Pane Code for simulation; OCBA and EA Simulation Run; Optimal Computing Budget Allocation Simulation; Equal Allocation (EA) Simulation; Graph Generation; APPENDIX B; SIMULATION OF MEAN AND STANDARD DEVIATION; Calculation of Growth Rate; Department Wise Budget Allocation; Percentage Growth Rate Calculation; APPENDIX C; BUDGET ALLOCATION USING GENETIC ALGORITHM APPROACH FITNESS CALCULATION; GA Algorithm.
 Korte, B. H. (Bernhard H.), 1938 author.
 Sixth edition.  Berlin, Germany : Springer Nature, [2018]
 Description
 Book — xxi, 698 pages : illustrations ; 25 cm.
 Summary

 1 Introduction. 2 Graphs. 3 Linear Programming. 4 Linear Programming Algorithms. 5 Integer Programming. 6 Spanning Trees and Arborescences. 7 Shortest Paths. 8 Network Flows. 9 Minimum Cost Flows. 10 Maximum Matchings. 11 Weighted Matching. 12 bMatchings and T Joins. 13 Matroids. 14 Generalizations of Matroids. 15 NPCompleteness. 16 Approximation Algorithms. 17 The Knapsack Problem. 18 BinPacking. 19 Multicommodity Flows and EdgeDisjoint Paths. 20 Network Design Problems. 21 The Traveling Salesman Problem. 22 Facility Location. Indices.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA402.5 .K6665 2018  Unknown 
18. The GLOBAL Optimization Algorithm : newly updated with Java Implementation and Parallelization [2018]
 Bánhelyi, Balázs, author
 Cham : Springer, [2018]
 Description
 Book — 1 online resource Digital: text file; PDF.
 Summary

 1. Introduction. 
 2. Local search techniques.  3.The GLOBALJ framework. 
 4. Parallelization. 
 5. Example. 
 6. Appendix: Users manual.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Theodore, Louis, author.
 Boca Raton : CRC Press, Paylor & Francis Group, [2018]
 Description
 Book — 1 online resource.
 Sergeyev, Yaroslav D., 1963 author.
 New York, NY : Springer, [2017]
 Description
 Book — 1 online resource () : illustrations.
 Summary

 1. Lipschitz global optimization.
 2. Onedimensional algorithms and their accleration.
 3. Diagonal approach and efficient paritioning strategies.
 4. Global optimization algorithms based on the nonredundant partitions. References.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
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