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Advances in Stochastic and Deterministic Global Optimization / / edited by Panos M. Pardalos, Anatoly Zhigljavsky, Julius Žilinskas
| Advances in Stochastic and Deterministic Global Optimization / / edited by Panos M. Pardalos, Anatoly Zhigljavsky, Julius Žilinskas |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (XIII, 296 p. 69 illus., 42 illus. in color.) |
| Disciplina | 510 |
| Collana | Springer Optimization and Its Applications |
| Soggetto topico |
Mathematical optimization
Probabilities Algorithms Numerical analysis Computer science - Mathematics Optimization Probability Theory Numerical Analysis Mathematical Applications in Computer Science |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part I: Theory and Algorithms for Global Optimization -- On the Asymptotic Tractability of Global Optimization -- Combining Interval and Probabilistic Uncertainty: What Is Computable? -- Survey of Piecewise Convex Maximization and PCMP over Spherical Sets -- Assessing Basin Identification Methods for Locating Multiple Optima -- Part II: Applications of Global Optimization -- Cloud Computing Approach for Intelligent Visualization of Multidimensional Data -- Comparative Study of Different Penalty Functions and Algorithms in Survey Calibration -- Multidimensional Scaling for Genomic Data -- Solving Stochastic Ship Fleet Routing Problems with Inventory Management Using Branch and Price -- Investigation of Data Regularization and Optimization of Timetables by Lithuanian High Schools Example -- Dynamic Global Optimisation Methods for Determining Guaranteed Solutions in Chemical Engineering -- On the Least-Squares Fitting of Data by Sinusoids -- Part III: Multi-objective Global Optimization -- A Multicriteria Generalization of Bayesian Global Optimization -- Understanding the Impact of Constraints: a Rank Based Fitness Function for Evolutionary Methods -- Estimating the Pareto Front of a Hard Bi-criterion Competitive Facility Location Problem -- On Sampling Methods for Costly Multi-objective Black-box Optimization. |
| Record Nr. | UNINA-9910149490903321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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High-Dimensional Optimization : Set Exploration in the Non-Asymptotic Regime
| High-Dimensional Optimization : Set Exploration in the Non-Asymptotic Regime |
| Autore | Noonan Jack |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2024 |
| Descrizione fisica | 1 online resource (153 pages) |
| Altri autori (Persone) | ZhigljavskyAnatoly |
| Collana | SpringerBriefs in Optimization Series |
| ISBN |
9783031589096
9783031589089 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Introduction -- Contents -- Notation and Abbreviations -- 1 High-Dimensional Cubes, Balls and Spherically Symmetric Distributions -- 1.1 Spherically Symmetric and Beta Distributions -- 1.1.1 Beta-Distributed Random Variables -- 1.1.2 Uniform Distribution on a Sphere -- 1.1.3 Spherically Symmetric Random Vectors -- 1.1.4 Ball and Sphere -- 1.2 High-Dimensional Cube -- 1.2.1 Concentration of Mass in the Cube -- 1.2.2 Approximations for the Distribution of "026B30D U"026B30D 2 -- 1.3 The Squared Distance "026B30D X-Y"026B30D 2 When X Either Is Spherically Symmetric or Has i.i.d. Symmetric Components -- 1.3.1 Distribution of the Squared Distance When X Is Spherically Symmetric -- 1.3.2 The First Two Moments of "026B30D X-Y"026B30D 2 -- 1.3.3 The Third Moment of "026B30D X-Y"026B30D 2 -- 1.3.4 The Fourth Moment of "026B30D X-Y"026B30D 2 -- 1.4 Approximation of the Volume of Intersection of a Ball and a Cube -- 1.4.1 Variability in the Volumes of Intersection -- 1.4.2 Approximations with Spherically Symmetric Models -- 1.4.3 CLT-Based Approximations -- 1.4.4 Numerical Comparison of CLT-Based Approximations -- 1.4.5 Comparison of Approximations of Different Origins -- References -- 2 Space Exploration -- 2.1 Volume of Intersection of a Cube and n Balls -- 2.1.1 Covering and Partial Covering -- 2.1.2 Partial Covering: Asymptotic Considerations -- 2.1.3 Asymptotic Versus Non-asymptotic Regimes -- 2.1.4 The Family of Random Designs Considered -- 2.1.5 Approximations with Spherically Symmetric Models -- Approximations with One Ball -- Simulation Study -- 2.1.6 Approximations for n Balls -- Simulation Study -- 2.1.7 CLT-Based Approximations -- Approximation for One Ball -- Approximation for n Balls -- Simulation Study -- 2.2 Construction of Efficient Exploration Schemes -- 2.2.1 The Probability of Covering as a Function of α and δ.
2.2.2 Efficiency Plots -- 2.2.3 Practical Recommendations -- 2.3 Quantization -- 2.3.1 Bounds for Optimal Quantizers -- 2.3.2 Boundary Correction for Nearest Neighbor Distances -- 2.3.3 Approximating Quantization Error for Finite n -- Approximating Quantization Using Partial Covering -- Approximations Based on the Use of the Spherical Model -- Simulation Study -- CLT-Based Approximations for the Quantization Error -- Simulation Study -- Approximating Mean Squared Quantization Error Using Extreme Value Theory -- A Simple Approximation for Mean Squared Quantization Error -- Simulation Study -- 2.3.4 Efficient Exploration Designs for Quantization -- 2.3.5 Equivalence to the Problem of Partial Covering -- 2.4 Quantization Using the Checkerboard Lattice Points -- 2.4.1 Reformulation in Terms of the Voronoi Cells -- Re-normalization of the Quantization Error -- Voronoi Cells for Dn,δ -- 2.4.2 Explicit Formulae for the Quantization Error -- 2.4.3 Closed-Form Expressions for the Coverage Area -- Reduction to Voronoi Cells -- Expressing Fd(Dn,δ,r) Through Fd,Z(r) -- Simple Bounds for Fd(Dn,δ,r) -- Radius Required for Partial Covering Is Much Smaller than the Covering Radius -- Numerical Studies -- Quantization and Weak Covering Comparisons -- Accuracy of Covering Approximation and Dependence on δ -- Stochastic Dominance -- 2.4.4 The Checkerboard Lattice with Point at Zero -- An Auxiliary Result -- Normalised Mean Squared Quantization Error for Dn,δ,0(0) -- Quantization Error for the Design Dn,δ,0 -- Numerical Studies -- References. |
| Record Nr. | UNINA-9910865238103321 |
Noonan Jack
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| Cham : , : Springer International Publishing AG, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Singular Spectrum Analysis for Time Series / / by Nina Golyandina, Anatoly Zhigljavsky
| Singular Spectrum Analysis for Time Series / / by Nina Golyandina, Anatoly Zhigljavsky |
| Autore | Golyandina Nina |
| Edizione | [2nd ed. 2020.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (IX, 146 p. 44 illus., 38 illus. in color.) |
| Disciplina | 519.55 |
| Collana | SpringerBriefs in Statistics |
| Soggetto topico |
Statistics
Signal processing Biometry Statistical Theory and Methods Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences Signal, Speech and Image Processing Statistics in Business, Management, Economics, Finance, Insurance Biostatistics |
| ISBN | 3-662-62436-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Introduction -- 1.1 Overview of SSA methodology and the structure of the book -- 1.2 SSA and other techniques -- 1.3 Computer implementation of SSA -- 1.4 Historical and bibliographical remarks -- 1.5 Common symbols and acronyms -- 2 Basic SSA - 2.1 The main algorithm -- 2.2 Potential of Basic SSA -- 2.3 Models of time series and SSA objectives -- 2.4 Choice of parameters in Basic SSA -- 2.5 Some variations of Basic SSA -- 2.6 Multidimensional and multivariate extensions of SSA -- 3 SSA for forecasting, interpolation, filtering and estimation -- 3.1 SSA forecasting algorithms -- 3.2 LRR and associated characteristic polynomials -- 3.3 Recurrent forecasting as approximate continuation -- 3.4 Confidence bounds for the forecasts -- 3.5 Summary and recommendations on forecasting parameters -- 3.6 Case study: ‘Fortified wine’ -- 3.7 Imputation of missing values -- 3.8 Subspace-based methods and estimation of signal parameters -- 3.9 SSA and filters -- 3.10 Multidimensional/Multivariate SSA. |
| Record Nr. | UNINA-9910863136103321 |
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Golyandina Nina
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| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Singular Spectrum Analysis for Time Series / / by Nina Golyandina, Anatoly Zhigljavsky
| Singular Spectrum Analysis for Time Series / / by Nina Golyandina, Anatoly Zhigljavsky |
| Autore | Golyandina Nina |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013 |
| Descrizione fisica | 1 online resource (125 p.) |
| Disciplina | 330.1951 |
| Altri autori (Persone) | ZhigljavskyAnatoly |
| Collana | SpringerBriefs in Statistics |
| Soggetto topico |
Statistics
Statistical Theory and Methods |
| ISBN |
1-299-19783-3
3-642-34913-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction: Preliminaries -- SSA Methodology and the Structure of the Book -- SSA Topics Outside the Scope of this Book -- Common Symbols and Acronyms -- Basic SSA: The Main Algorithm -- Potential of Basic SSA -- Models of Time Series and SSA Objectives -- Choice of Parameters in Basic SSA -- Some Variations of Basic SSA -- SSA for Forecasting, interpolation, Filtration and Estimation: SSA Forecasting Algorithms -- LRR and Associated Characteristic Polynomials -- Recurrent Forecasting as Approximate Continuation -- Confidence Bounds for the Forecast -- Summary and Recommendations on Forecasting Parameters -- Case Study: ‘Fortified Wine’ -- Missing Value Imputation -- Subspace-Based Methods and Estimation of Signal Parameters -- SSA and Filters. |
| Record Nr. | UNINA-9910438140003321 |
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Golyandina Nina
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| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Singular Spectrum Analysis with R / / by Nina Golyandina, Anton Korobeynikov, Anatoly Zhigljavsky
| Singular Spectrum Analysis with R / / by Nina Golyandina, Anton Korobeynikov, Anatoly Zhigljavsky |
| Autore | Golyandina Nina |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XIII, 272 p. 121 illus., 106 illus. in color.) |
| Disciplina | 519.5 |
| Collana | Use R! |
| Soggetto topico |
Statistics
Image processing - Digital techniques Computer vision Computer software Mathematical statistics - Data processing Biometry Statistical Theory and Methods Computer Imaging, Vision, Pattern Recognition and Graphics Mathematical Software Statistics in Business, Management, Economics, Finance, Insurance Statistics and Computing Biostatistics |
| ISBN | 3-662-57380-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface -- Common symbols and acronyms -- Contents -- 1 Introduction: Overview -- 2 SSA analysis of one-dimensional time series -- 3 Parameter estimation, forecasting, gap filling -- 4 SSA for multivariate time series -- 5 Image processing -- Index -- References. |
| Record Nr. | UNINA-9910300115803321 |
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Golyandina Nina
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| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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