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An introduction to statistics with Python : with applications in the life sciences / / Thomas Haslwanter
| An introduction to statistics with Python : with applications in the life sciences / / Thomas Haslwanter |
| Autore | Haslwanter Thomas <1964-> |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (341 pages) |
| Disciplina | 610.727 |
| Collana | Statistics and Computing |
| Soggetto topico |
Biometry
Computer science - Mathematics Programming languages (Electronic computers) Python (Llenguatge de programació) Estadística matemàtica Processament de dades Biometria |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783030973711
9783030973704 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910631094303321 |
Haslwanter Thomas <1964->
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| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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An introduction to statistics with Python : with applications in the life sciences / / Thomas Haslwanter
| An introduction to statistics with Python : with applications in the life sciences / / Thomas Haslwanter |
| Autore | Haslwanter Thomas <1964-> |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (341 pages) |
| Disciplina | 610.727 |
| Collana | Statistics and Computing |
| Soggetto topico |
Biometry
Computer science - Mathematics Programming languages (Electronic computers) Python (Llenguatge de programació) Estadística matemàtica Processament de dades Biometria |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783030973711
9783030973704 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996499869703316 |
|
Haslwanter Thomas <1964->
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| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines [[electronic resource] ] : Theory, Algorithms and Applications / / edited by Jamal Amani Rad, Kourosh Parand, Snehashish Chakraverty
| Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines [[electronic resource] ] : Theory, Algorithms and Applications / / edited by Jamal Amani Rad, Kourosh Parand, Snehashish Chakraverty |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (XIV, 305 p. 83 illus., 58 illus. in color.) |
| Disciplina | 512.3 |
| Collana | Industrial and Applied Mathematics |
| Soggetto topico |
Algebraic fields
Polynomials Mathematical optimization Quantitative research Machine learning Pattern recognition systems Python (Computer program language) Field Theory and Polynomials Optimization Data Analysis and Big Data Machine Learning Automated Pattern Recognition Python Aprenentatge automàtic Algorismes Funcions de Kernel Python (Llenguatge de programació) |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-19-6553-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction to SVM -- Basics of SVM Method and Least Squares SVM -- Fractional Chebyshev Kernel Functions: Theory and Application -- Fractional Legendre Kernel Functions: Theory and Application -- Fractional Gegenbauer Kernel Functions: Theory and Application -- Fractional Jacobi Kernel Functions: Theory and Application -- Solving Ordinary Differential Equations by LS-SVM -- Solving Partial Differential Equations by LS-SVM -- Solving Integral Equations by LS-SVR -- Solving Distributed-Order Fractional Equations by LS-SVR -- GPU Acceleration of LS-SVM, Based on Fractional Orthogonal Functions -- Classification Using Orthogonal Kernel Functions: Tutorial on ORSVM Package. |
| Record Nr. | UNINA-9910682554503321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Linear Algebra with Python : Theory and Applications / / by Makoto Tsukada, Yuji Kobayashi, Hiroshi Kaneko, Sin-Ei Takahasi, Kiyoshi Shirayanagi, Masato Noguchi
| Linear Algebra with Python : Theory and Applications / / by Makoto Tsukada, Yuji Kobayashi, Hiroshi Kaneko, Sin-Ei Takahasi, Kiyoshi Shirayanagi, Masato Noguchi |
| Autore | Tsukada Makoto |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (315 pages) |
| Disciplina | 512.502855133 |
| Altri autori (Persone) |
KobayashiYuji
KanekoHiroshi TakahasiSin-Ei ShirayanagiKiyoshi NoguchiMasato |
| Collana | Springer Undergraduate Texts in Mathematics and Technology |
| Soggetto topico |
Algebras, Linear
Functional analysis Python (Computer program language) Anàlisi funcional Àlgebra lineal Python (Llenguatge de programació) Linear Algebra Functional Analysis Python |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-9929-51-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Mathematics and Python -- Linear Spaces and Linear Mappings -- Basis and Dimension -- Matrices -- Elementary Operations and Matrix Invariants -- Inner Product and Fourier Expansion -- Eigenvalues and Eigenvectors -- Jordan Normal Form and Spectrum -- Dynamical Systems -- Applications and Development of Linear Algebra. |
| Record Nr. | UNINA-9910770272303321 |
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Tsukada Makoto
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Modern Statistics [[electronic resource] ] : A Computer-Based Approach with Python / / by Ron S. Kenett, Shelemyahu Zacks, Peter Gedeck
| Modern Statistics [[electronic resource] ] : A Computer-Based Approach with Python / / by Ron S. Kenett, Shelemyahu Zacks, Peter Gedeck |
| Autore | Kenett Ron S. |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Birkhäuser, , 2022 |
| Descrizione fisica | 1 online resource (453 pages) |
| Disciplina | 005.133 |
| Collana | Statistics for Industry, Technology, and Engineering |
| Soggetto topico |
Mathematical statistics - Data processing
Statistics Artificial intelligence - Data processing Industrial engineering Production engineering Statistics and Computing Statistical Theory and Methods Data Science Industrial and Production Engineering Estadística Processament de dades Python (Llenguatge de programació) |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-07566-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Analyzing Variability: Descriptive Statistics -- Probability Models and Distribution Functions -- Statistical Inference and Bootstrapping -- Variability in Several Dimensions and Regression Models -- Sampling for Estimation of Finite Population Quantities -- Time Series Analysis and Prediction -- Modern analytic methods: Part I -- Modern analytic methods: Part II -- Introduction to Python -- List of Python packages -- Code Repository and Solution Manual -- Bibliography -- Index. |
| Record Nr. | UNISA-996490346003316 |
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Kenett Ron S.
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| Cham : , : Springer International Publishing : , : Imprint : Birkhäuser, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Modern Statistics [[electronic resource] ] : A Computer-Based Approach with Python / / by Ron S. Kenett, Shelemyahu Zacks, Peter Gedeck
| Modern Statistics [[electronic resource] ] : A Computer-Based Approach with Python / / by Ron S. Kenett, Shelemyahu Zacks, Peter Gedeck |
| Autore | Kenett Ron S. |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Birkhäuser, , 2022 |
| Descrizione fisica | 1 online resource (453 pages) |
| Disciplina | 005.133 |
| Collana | Statistics for Industry, Technology, and Engineering |
| Soggetto topico |
Mathematical statistics - Data processing
Statistics Artificial intelligence - Data processing Industrial engineering Production engineering Statistics and Computing Statistical Theory and Methods Data Science Industrial and Production Engineering Estadística Processament de dades Python (Llenguatge de programació) |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-07566-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Analyzing Variability: Descriptive Statistics -- Probability Models and Distribution Functions -- Statistical Inference and Bootstrapping -- Variability in Several Dimensions and Regression Models -- Sampling for Estimation of Finite Population Quantities -- Time Series Analysis and Prediction -- Modern analytic methods: Part I -- Modern analytic methods: Part II -- Introduction to Python -- List of Python packages -- Code Repository and Solution Manual -- Bibliography -- Index. |
| Record Nr. | UNINA-9910595043803321 |
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Kenett Ron S.
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| Cham : , : Springer International Publishing : , : Imprint : Birkhäuser, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Modern Survey Analysis [[electronic resource] ] : Using Python for Deeper Insights / / by Walter R. Paczkowski
| Modern Survey Analysis [[electronic resource] ] : Using Python for Deeper Insights / / by Walter R. Paczkowski |
| Autore | Paczkowski Walter R. |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (365 pages) |
| Disciplina | 382.072 |
| Soggetto topico |
Statistics
Information visualization Marketing Mathematical statistics - Data processing Artificial intelligence - Data processing Statistics in Business, Management, Economics, Finance, Insurance Data and Information Visualization Statistics and Computing Data Science Estudis de mercat Processament de dades Python (Llenguatge de programació) |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783030762674
9783030762667 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Introduction -- 2. Understanding the structure of survey data -- 3. Shallow analyses of survey data -- 4. Deep analyses of survey data -- 5. Conclusion and wrap-up. |
| Record Nr. | UNISA-996490346603316 |
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Paczkowski Walter R.
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Modern Survey Analysis [[electronic resource] ] : Using Python for Deeper Insights / / by Walter R. Paczkowski
| Modern Survey Analysis [[electronic resource] ] : Using Python for Deeper Insights / / by Walter R. Paczkowski |
| Autore | Paczkowski Walter R. |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (365 pages) |
| Disciplina | 382.072 |
| Soggetto topico |
Statistics
Information visualization Marketing Mathematical statistics - Data processing Artificial intelligence - Data processing Statistics in Business, Management, Economics, Finance, Insurance Data and Information Visualization Statistics and Computing Data Science Estudis de mercat Processament de dades Python (Llenguatge de programació) |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783030762674
9783030762667 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Introduction -- 2. Understanding the structure of survey data -- 3. Shallow analyses of survey data -- 4. Deep analyses of survey data -- 5. Conclusion and wrap-up. |
| Record Nr. | UNINA-9910592992903321 |
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Paczkowski Walter R.
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 |
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Bynum Michael L.
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 |
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Bynum Michael L.
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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