Stochastic optimization

Stochastic Optimization Algorithms have become essential tools in solving a wide range of difficult and critical optimization problems. Such methods are able to find the optimum solution of a problem with uncertain elements or to algorithmically incorporate uncertainty to solve a deterministic problem. They even succeed in â€œfighting uncertainty with uncertaintyâ€. This book discusses theoretical aspects of many such algorithms and covers their application in various scientific fields
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Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations.
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Stochastic Optimality Theory (Boersma, 1997) is a widelyused model in linguistics that did not have a theoretically sound learning method previously. In this paper, a Markov chain MonteCarlo method is proposed for learning Stochastic OT Grammars. Following a Bayesian framework, the goal is ﬁnding the posterior distribution of the grammar given the relative frequencies of inputoutput pairs. The Data Augmentation algorithm allows one to simulate a joint posterior distribution by iterating two conditional sampling steps. ...
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Ant Colony Optimization (ACO) is the best example of how studies aimed at understanding and modeling the behavior of ants and other social insects can provide inspiration for the development of computational algorithms for the solution of difficult mathematical problems. Introduced by Marco Dorigo in his PhD thesis (1992) and initially applied to the travelling salesman problem, the ACO field has experienced a tremendous growth, standing today as an important natureinspired stochastic metaheuristic for hard optimization problems....
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The purpose of this study was to investigate the operation of the Hoabinh reservoir in the Red River Basin of Vietnam, and assess the room for its improvement by application of system analysis and optimal control techniques. The study aimed at establishing a foundation for further research on interreservoir regulation of the Basin. Finally, this study provided a testing ground for developing and comparing dierent reservoir optimization methods.
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Stochastic control plays an important role in many scientific and applied disciplines including communications, engineering, medicine, finance and many others. It is one of the effective methods being used to find optimal decisionmaking strategies in applications. The book provides a collection of outstanding investigations in various aspects of stochastic systems and their behavior. The book provides a selfcontained treatment on practical aspects of stochastic modeling and calculus including applications drawn from engineering, statistics, and computer science....
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We propose WIDLexpressions as a ﬂexible formalism that facilitates the integration of a generic sentence realization system within endtoend language processing applications. WIDLexpressions represent compactly probability distributions over ﬁnite sets of candidate realizations, and have optimal algorithms for realization via interpolation with language model probability distributions. We show the effectiveness of a WIDLbased NLG system in two sentence realization tasks: automatic translation and headline generation. ...
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This book presents some of the main ideas of game theory. It is designed to serve as a textbook for a onesemester graduate course consisting of about 28 meetings each of 90 minutes. The topics that we cover are those that we personally would include in such a onesemester course. We do not pretend to provide a complete reference book on game theory and do not necessarily regard the topics that we exclude as unimportant. Our selection inevitably reflects our own preferences and interests.
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This book presents and develops major numerical methods currently used for solving problems arising in quantitative finance. Our presentation splits into two parts. Part I is methodological, and offers a comprehensive toolkit on numerical methods and algorithms. This includes Monte Carlo simulation, numerical schemes for partial differential equations, stochastic optimization in discrete time, copula functions, transformbased methods and quadrature techniques. Part II is practical, and features a number of selfcontained cases.
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Parametric representation of shapes, mechanical components modeling with 3D visualization techniques using object oriented programming, the well known golden ratio application on vertical and horizontal displacement investigations of the ground surface, spatial modeling and simulating of dynamic continuous fluid flow process, simulation model for wastewater treatment, an interaction of tilt and illumination conditions at flight simulation and errors in taxiing performance, plant layout optimal plot plan, atmospheric modeling for weather prediction, a stochastic search method that explores ...
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Mastering the concepts of probability can cast new light on situations in which randomness and chance appear to rule. With an emphasis on why probability works and how it can be applied, Henk Tijms introduces the reader to the world of probability in an informal way. Lotteries and casino games provide a natural source of motivation, and he carefully discusses these with many worked examples to illustrate the key concepts from probability theory. Henk Tijms has been a professor in Operations Research at the Vrije University in Amsterdam since 1976.
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In this chapter, we study the mathematical structure of a simple oneperiod model of a financial market. We consider a finite number of assets. Their initial prices at time t = 0 are known, their future prices at time t = 1 are described as random variables on some probability space. Trading takes place at time t = 0. Already in this simple model, some basic principles of mathematical finance appear very clearly. In Section 1.2, we single out those models which satisfy a condition of market efficiency: There are no trading opportunities which yield a profit without any downside risk.
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Stochastic Calculus of Variations (or Malliavin Calculus) consists, in brief, in constructing and exploiting natural differentiable structures on abstract probability spaces; in other words, Stochastic Calculus of Variations proceeds from a merging of differential calculus and probability theory. As optimization under a random environment is at the heart of mathematical finance, and as differential calculus is of paramount importance for the search of extrema, it is not surprising that Stochastic Calculus of Variations appears in mathematical finance.
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These notes build upon a course I taught at the University of Maryland during the fall of 1983. My great thanks go to Martino Bardi, who took careful notes, saved them all these years and recently mailed them to me. Faye Yeager typed up his notes into a first draft of these lectures as they now appear.
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Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Optimal Harvest of a Stochastic PredatorPrey Model
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To introduce the ]orwardbackward stochastic differential equations (FBS DEs, for short), let us begin with some examples. Unless otherwise speci fled, throughout the book, we let (~, •, {Ft)t_0, P) be a complete filtered probability space on which is defined a ddimensional standard Brownian motion W(t), such that {5~t }t_0 is the natural filtration of W(t), augmented by...
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Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Hybrid Method for a Class of Stochastic BiCriteria Optimization Problems
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Continuoustime modeling in finance, though introduced by Louis Bachelier's 1900 thesis on the theory of speculation, really started with Merton's seminal work in the 1970s. Since then, the continuoustime paradigm has proved to be an immensely useful tool in finance and more generally economics. Continuoustime models are widely used to study issues that include the decision to optimally consume, save, and invest, portfolio choice under a variety of constraints, contingent claim pricing, capital accumulation, resource extraction, game theory, and more recently contract theory....
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This paper develops theory missing in the sizeable literature that uses data envelopment analysis to construct return : risk ratios for investment funds. It explores the production possibility set of the investment funds to identify an appropriate form of returns to scale. It discusses what risk and return measures can justiﬁably be combined and how to deal with negative risks, and identiﬁes suitable sets of measures. It identiﬁes the problems of failing to deal with diversiﬁcation and develops an iterative approximation procedure to deal with it.
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Modern game theory has evolved enormously since its inception in the 1920s in the works of Borel and von Neumann. The branch of game theory known as dynamic games descended from the pioneering work on differential games by R. Isaacs, L. S. Pontryagin and his school, and from seminal papers on extensive form games by Kuhn and on stochastic games by Shapley.
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