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Intro; Preface; Contents; Contributors; Introduction; 1 Survey; Evaluation Complexity Bounds for Smooth Constrained Nonlinear Optimization Using Scaled KKT Conditions and High-Order Models; 1 Introduction; 2 Convex Constraints; 3 The General Constrained Case; 4 Discussion; References; Data-Dependent Approximation in Social Computing; 1 Introduction; 2 Example; 3 Theoretical Notes; 4 Conclusion; References; Multi-Objective Evolutionary Optimization Algorithms for Machine Learning: A Recent Survey; 1 Introduction; 2 Basic Concepts of Multi-Objective Optimization; 3 Data Preprocessing
4 Supervised Learning5 Unsupervised Learning; 6 A Few of the Most Recent Applications; 7 Synopsis and Discussion; References; No Free Lunch Theorem: A Review; 1 Introduction; 2 Early Developments; 3 No Free Lunch for Optimization and Search; 4 More Recent Work of Wolpert; 5 NFL for Optimization and Evolutionary Algorithms; 5.1 No Free Lunches and Evolutionary Algorithms; 5.2 No Free Lunches and Meta-Heuristic Techniques; 6 NFL for Supervised Learning; 6.1 No Free Lunch for Early Stopping; 6.2 No Free Lunch for Cross-Validation
6.3 Real-World Machine Learning Classification and No Free Lunch Theorems: An Experimental Approach7 Synopsis and Concluding Remarks; References; Piecewise Convex-Concave Approximation in the Minimax Norm; 1 Introduction; 2 The Algorithm; 2.1 The Case q=0; 2.2 The Case q=1; 2.3 The Case q=2; 2.4 The General Case; 3 Numerical Results and Conclusions; 3.1 Synthetic Test Data; 3.2 Real Test Data; 3.3 Conclusion; References; A Decomposition Theorem for the Least Squares Piecewise Monotonic Data Approximation Problem; 1 Introduction; 2 The Theorem; 3 Estimation of Peaks of an NMR Spectrum
4 SummaryReferences; Recent Progress in Optimization of Multiband Electrical Filters; 1 History and Background; 2 Optimization Problem for Multiband Filter; 2.1 Four Settings; 2.1.1 Minimal Deviation; 2.1.2 Minimal Modified Deviation; 2.1.3 Third Zolotarëv Problem; 2.1.4 Fourth Zolotarëv Problem; 2.2 Study of Optimization Problem; 3 Zolotarëv Fraction; 4 Projective View; 4.1 Projective Problem Setting; 4.2 Decomposition into Subclasses; 4.3 Extremal Problem for Classes; 4.4 Equiripple Property; 5 Problem Genesis: Signal Processing; 6 Approaches to Optimization; 6.1 Remez-Type Methods
6.2 Composite Filters6.3 Ansatz Method; 7 Novel Analytical Approach; 8 Examples of Filter Design; References; Impact of Error in Parameter Estimations on Large Scale Portfolio Optimization; 1 Introduction; 2 Theoretical Background; 2.1 Portfolio Optimization; 2.1.1 Markowitz Model and Its Variations; 2.1.2 Single-Factor Model; 2.1.3 Multi-Factor Model; 2.2 Parameters Estimation; 2.2.1 Estimation of Means; 2.2.2 Estimation of Covariances; 2.2.3 Ledoit and Wolf Shrinkage Estimator for Covariance Matrix; 3 Properties of Selected Portfolios; 3.1 Risk of Selected Portfolios; 3.1.1 Real Data; 3.1.2 Generated Data
4 Supervised Learning5 Unsupervised Learning; 6 A Few of the Most Recent Applications; 7 Synopsis and Discussion; References; No Free Lunch Theorem: A Review; 1 Introduction; 2 Early Developments; 3 No Free Lunch for Optimization and Search; 4 More Recent Work of Wolpert; 5 NFL for Optimization and Evolutionary Algorithms; 5.1 No Free Lunches and Evolutionary Algorithms; 5.2 No Free Lunches and Meta-Heuristic Techniques; 6 NFL for Supervised Learning; 6.1 No Free Lunch for Early Stopping; 6.2 No Free Lunch for Cross-Validation
6.3 Real-World Machine Learning Classification and No Free Lunch Theorems: An Experimental Approach7 Synopsis and Concluding Remarks; References; Piecewise Convex-Concave Approximation in the Minimax Norm; 1 Introduction; 2 The Algorithm; 2.1 The Case q=0; 2.2 The Case q=1; 2.3 The Case q=2; 2.4 The General Case; 3 Numerical Results and Conclusions; 3.1 Synthetic Test Data; 3.2 Real Test Data; 3.3 Conclusion; References; A Decomposition Theorem for the Least Squares Piecewise Monotonic Data Approximation Problem; 1 Introduction; 2 The Theorem; 3 Estimation of Peaks of an NMR Spectrum
4 SummaryReferences; Recent Progress in Optimization of Multiband Electrical Filters; 1 History and Background; 2 Optimization Problem for Multiband Filter; 2.1 Four Settings; 2.1.1 Minimal Deviation; 2.1.2 Minimal Modified Deviation; 2.1.3 Third Zolotarëv Problem; 2.1.4 Fourth Zolotarëv Problem; 2.2 Study of Optimization Problem; 3 Zolotarëv Fraction; 4 Projective View; 4.1 Projective Problem Setting; 4.2 Decomposition into Subclasses; 4.3 Extremal Problem for Classes; 4.4 Equiripple Property; 5 Problem Genesis: Signal Processing; 6 Approaches to Optimization; 6.1 Remez-Type Methods
6.2 Composite Filters6.3 Ansatz Method; 7 Novel Analytical Approach; 8 Examples of Filter Design; References; Impact of Error in Parameter Estimations on Large Scale Portfolio Optimization; 1 Introduction; 2 Theoretical Background; 2.1 Portfolio Optimization; 2.1.1 Markowitz Model and Its Variations; 2.1.2 Single-Factor Model; 2.1.3 Multi-Factor Model; 2.2 Parameters Estimation; 2.2.1 Estimation of Means; 2.2.2 Estimation of Covariances; 2.2.3 Ledoit and Wolf Shrinkage Estimator for Covariance Matrix; 3 Properties of Selected Portfolios; 3.1 Risk of Selected Portfolios; 3.1.1 Real Data; 3.1.2 Generated Data