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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 Computer mathematics Optimization Probability Theory and Stochastic Processes 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 / / Nina Golyandina, Anatoly Zhigljavsky
| Singular spectrum analysis for time series / / Nina Golyandina, Anatoly Zhigljavsky |
| Autore | Golyandina Nina |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | New York, : Springer, 2013 |
| Descrizione fisica | 1 online resource (125 p.) |
| Disciplina | 330.1951 |
| Altri autori (Persone) | ZhigljavskyAnatoly |
| Collana | SpringerBriefs in statistics |
| Soggetto topico |
Time-series analysis
Spectral theory (Mathematics) Decomposition (Mathematics) |
| 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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| New York, : 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
Optical data processing Computer software R (Computer language program) Statistical Theory and Methods Computer Imaging, Vision, Pattern Recognition and Graphics Mathematical Software Statistics for Business, Management, Economics, Finance, Insurance Statistics and Computing/Statistics Programs Statistics for Life Sciences, Medicine, Health Sciences |
| 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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