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Modern Applied Statistics With S

List of notations Preface to the second edition Preface to the first edition 1. Stochastic programming models 2. Two-stage problems 3. Multistage problems 4. Optimization models with probabilistic constraints 5. Statistical inference 6. Risk averse optimization 7. Background material 8. Bibliographical remarks Bibliography Index.

Lectures on Stochastic Programming: Modeling and Theory

本文对国际科学计量学杂志《Ｓｃｉｅｎｔｏｍｅｔｒｉｃｓ》１９７９－１９９１年的研究论文内容、栏目、作者及国别和编委及国别作了计量分析，揭示出科学计量学研究的重点、活动的中心及发展趋势，说明了学科带头人在发展科学计量学这门新兴学科中的作用。

国际刊物《Scientometrics》文献计量研究

EvoCOMNET Contributions.- New Research in Nature Inspired Algorithms for Mobility Management in GSM Networks.- Adaptive Local Search for a New Military Frequency Hopping Planning Problem.- SS vs PBIL to Solve a Real-World Frequency Assignment Problem in GSM Networks.- Reconstruction of Networks from Their Betweenness Centrality.- A Self-learning Optimization Technique for Topology Design of Computer Networks.- A Comparative Study of Fuzzy Inference Systems, Neural Networks and Adaptive Neuro Fuzzy Inference Systems for Portscan Detection.- EvoFIN Contributions.- Evolutionary Single-Position Automated Trading.- Genetic Programming in Statistical Arbitrage.- Evolutionary System for Generating Investment Strategies.- Horizontal Generalization Properties of Fuzzy Rule-Based Trading Models.- Particle Swarm Optimization for Tackling Continuous Review Inventory Models.- Option Model Calibration Using a Bacterial Foraging Optimization Algorithm.- A SOM and GP Tool for Reducing the Dimensionality of a Financial Distress Prediction Problem.- Quantum-Inspired Evolutionary Algorithms for Financial Data Analysis.- EvoHOT Contributions.- Analysis of Reconfigurable Logic Blocks for Evolvable Digital Architectures.- Analogue Circuit Control through Gene Expression.- Discovering Several Robot Behaviors through Speciation.- Architecture Performance Prediction Using Evolutionary Artificial Neural Networks.- Evolving a Vision-Driven Robot Controller for Real-World Indoor Navigation.- Evolving an Automatic Defect Classification Tool.- Deterministic Test Pattern Generator Design.- An Evolutionary Methodology for Test Generation for Peripheral Cores Via Dynamic FSM Extraction.- Exploiting MOEA to Automatically Geneate Test Programs for Path-Delay Faults in Microprocessors.- EvoIASP Contributions.- Evolutionary Object Detection by Means of Naive Bayes Models Estimation.- An Evolutionary Framework for Colorimetric Characterization of Scanners.- Artificial Creatures for Object Tracking and Segmentation.- Automatic Recognition of Hand Gestures with Differential Evolution.- Optimizing Computed Tomographic Angiography Image Segmentation Using Fitness Based Partitioning.- A GA-Based Feature Selection Algorithm for Remote Sensing Images.- An Evolutionary Approach for Ontology Driven Image Interpretation.- Hybrid Genetic Algorithm Based on Gene Fragment Competition for Polyphonic Music Transcription.- Classification of Seafloor Habitats Using Genetic Programming.- Selecting Local Region Descriptors with a Genetic Algorithm for Real-World Place Recognition.- Object Detection Using Neural Networks and Genetic Programming.- Direct 3D Metric Reconstruction from Multiple Views Using Differential Evolution.- Discrete Tomography Reconstruction through a New Memetic Algorithm.- A Fuzzy Hybrid Method for Image Decomposition Problem.- Triangulation Using Differential Evolution.- Fast Multi-template Matching Using a Particle Swarm Optimization Algorithm for PCB Inspection.- EvoMUSART Contributions.- A Generative Representation for the Evolution of Jazz Solos.- Automatic Invention of Fitness Functions with Application to Scene Generation.- Manipulating Artificial Ecosystems.- Evolved Diffusion Limited Aggregation Compositions.- Scaffolding for Interactively Evolving Novel Drum Tracks for Existing Songs.- AtomSwarm: A Framework for Swarm Improvisation.- Using DNA to Generate 3D Organic Art Forms.- Towards Music Fitness Evaluation with the Hierarchical SOM.- Evolutionary Pointillist Modules: Evolving Assemblages of 3D Objects.- An Artificial-Chemistry Approach to Generating Polyphonic Musical Phrases.- Implicit Fitness Functions for Evolving a Drawing Robot.- Free Flight in Parameter Space: A Dynamic Mapping Strategy for Expressive Free Impro.- Modelling Video Games' Landscapes by Means of Genetic Terrain Programming - A New Approach for Improving Users' Experience.- Virtual Constructive Swarm Compositions and Inspirations.- New-Generation Methods in an Interpolating EC Synthesizer Interface.- Composing Music with Neural Networks and Probabilistic Finite-State Machines.- TransFormer #13: Exploration and Adaptation of Evolution Expressed in a Dynamic Sculpture.- EvoNUM Contributions.- Multiobjective Tuning of Robust PID Controllers Using Evolutionary Algorithms.- Truncation Selection and Gaussian EDA: Bounds for Sustainable Progress in High-Dimensional Spaces.- Scalable Continuous Multiobjective Optimization with a Neural Network-Based Estimation of Distribution Algorithm.- Cumulative Step Length Adaptation for Evolution Strategies Using Negative Recombination Weights.- Computing Surrogate Constraints for Multidimensional Knapsack Problems Using Evolution Strategies.- A Critical Assessment of Some Variants of Particle Swarm Optimization.- An Evolutionary Game-Theoretical Approach to Particle Swarm Optimisation.- A Hybrid Particle Swarm Optimization Algorithm for Function Optimization.- EvoSTOC Contributions.- Memory Based on Abstraction for Dynamic Fitness Functions.- A Memory Enhanced Evolutionary Algorithm for Dynamic Scheduling Problems.- Compound Particle Swarm Optimization in Dynamic Environments.- An Evolutionary Algorithm for Adaptive Online Services in Dynamic Environment.- EvoTHEORY Contributions.- A Study of Some Implications of the No Free Lunch Theorem.- Negative Slope Coefficient and the Difficulty of Random 3-SAT Instances.- EvoTRANSLOG Contributions.- A Memetic Algorithm for the Team Orienteering Problem.- Decentralized Evolutionary Optimization Approach to the p-Median Problem.- Genetic Computation of Road Network Design and Pricing Stackelberg Games with Multi-class Users.- Constrained Local Search Method for Bus Fleet Scheduling Problem with Multi-depot with Line Change.- Evolutionary System with Precedence Constraints for Ore Harbor Schedule Optimization.

Applications of evolutionary computing: EvoWorkshops 2008: EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Naples, Italy, March 26-28, 2008. Proceedings.

3D scene analysis in terms of automatically assigning 3D points a respective semantic label has become a topic of great importance in photogrammetry, remote sensing, computer vision and robotics. In this paper, we address the issue of how to increase the distinctiveness of geometric features and select the most relevant ones among these for 3D scene analysis. We present a new, fully automated and versatile framework composed of four components: (i) neighborhood selection, (ii) feature extraction, (iii) feature selection and (iv) classification. For each component, we consider a variety of approaches which allow applicability in terms of simplicity, efficiency and reproducibility, so that end-users can easily apply the different components and do not require expert knowledge in the respective domains. In a detailed evaluation involving 7 neighborhood definitions, 21 geometric features, 7 approaches for feature selection, 10 classifiers and 2 benchmark datasets, we demonstrate that the selection of optimal neighborhoods for individual 3D points significantly improves the results of 3D scene analysis. Additionally, we show that the selection of adequate feature subsets may even further increase the quality of the derived results while significantly reducing both processing time and memory consumption.

Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers

The quality of multi-stage stochastic optimization models as they appear in asset liability management, energy planning, transportation, supply chain management, and other applications depends heavily on the quality of the underlying scenario model, describing the uncertain processes influencing the profit/cost function, such as asset prices and liabilities, the energy demand process, demand for transportation, and the like. A common approach to generate scenarios is based on estimating an unknown distribution and matching its moments with moments of a discrete scenario model. This paper demonstrates that the problem of finding valuable scenario approximations can be viewed as the problem of optimally approximating a given distribution with some distance function. We show that for Lipschitz continuous cost/profit functions it is best to employ the Wasserstein distance. The resulting optimization problem can be viewed as a multi-dimensional facility location problem, for which at least good heuristic algorithms exist. For multi-stage problems, a scenario tree is constructed as a nested facility location problem. Numerical convergence results for financial mean-risk portfolio selection conclude the paper.

https://homepage.univie.ac.at/georg.pflug/science/technicalreports/hp-scenmultfac.pdf

Financial scenario generation for stochastic multi-stage decision processes as facility location problems

This paper presents a survey of georeferenced point clouds. Concentration is, on the one hand, put on features, which originate in the measurement process themselves, and features derived by processing the point cloud. On the other hand, approaches for the processing of georeferenced point clouds are reviewed. This includes the data structures, but also spatial processing concepts. We suggest a categorization of features into levels that reflect the amount of processing. Point clouds are found across many disciplines, which is reflected in the versatility of the literature suggesting specific features. (authors' abstract)

https://www.mdpi.com/2220-9964/2/4/1038/pdf

Georeferenced Point Clouds: A Survey of Features and Point Cloud Management

We formulate the service composition problem as a multi-objective stochastic program which simultaneously optimizes the following quality of service (QoS) parameters: workflow duration, service invocation costs, availability, and reliability. All of these quality measures are modelled as decision-dependent random variables. Our model minimizes the average value-at- risk (AVaR) of the workflow duration and costs while imposing constraints on the workflow availability and reliability. AVaR is a popular risk measure in decision theory which quantifies the expected shortfall below some percentile of a loss distribution. By replacing the random durations and costs with their expected values, our risk-aware model reduces to the nominal problem formulation prevalent in literature. We argue that this nominal model can lead to overly risky decisions. Finally, we report on the scalability properties of our model.

A Stochastic Programming Approach for QoS-Aware Service Composition

Due to increasing complexity and non-convexity of financial engineering problems, biologically inspired heuristic algorithms gained significant importance especially in the area of financial decision optimization. In this paper, the stochastic scenario-based risk-return portfolio optimization problem is analyzed and solved with an evolutionary computation approach. The advantage of applying this approach is the creation of a common framework for an arbitrary set of loss distribution-based risk measures, regardless of their underlying structure. Numerical results for three of the most commonly used risk measures conclude the paper.

An Evolutionary Computation Approach to Scenario-Based Risk-Return Portfolio Optimization for General Risk Measures

In this article, we present a representation of value-at-risk (VaR) as a difference of convex (D.C.) functions in the case where the distribution of the underlying random variable is discrete and has finitely many atoms. The D.C. representation is used to study a financial risk-return portfolio selection problem with a VaR constraint. A branch-and-bound algorithm that numerically solves the problem exactly is given. Numerical experiments with historical asset returns from representative market indices are performed to apply the algorithm to real-world financial market data.

http://www.optimization-online.org/DB_FILE/2008/01/1868.pdf

Ronald Hochreiter

Papers

Financial scenario generation for stochastic multi-stage decision processes as facility location problems

Georeferenced Point Clouds: A Survey of Features and Point Cloud Management

A Stochastic Programming Approach for QoS-Aware Service Composition

An Evolutionary Computation Approach to Scenario-Based Risk-Return Portfolio Optimization for General Risk Measures

A difference of convex formulation of value-at-risk constrained optimization