Search Results: 'convex optimization'

Search

×
×
×
×
6 results for "convex optimization"
Convex Optimization & Euclidean Distance Geometry By Jon Dattorro
Hardcover: $174.00
Ships in 6-8 business days.
Convex Analysis is the calculus of inequalities while Convex Optimization is its application. Analysis is inherently the domain of the mathematician while Optimization belongs to the engineer. In... More > layman’s terms, the mathematical science of Optimization is the study of how to make a good choice when confronted with conflicting requirements. The qualifier Convex means: when an optimal solution is found, then it is guaranteed to be a best solution; there is no better choice. As any Convex Optimization problem has geometric interpretation, this book is about Convex Optimization, convex geometry (with particular attention to distance geometry), and nonconvex, combinatorial, and geometrical problems that can be relaxed or transformed into convex problems. A virtual flood of new applications follows by epiphany that many problems, presumed nonconvex, can be so transformed. Color Hardcover< Less
Convex Optimization & Euclidean Distance Geometry By Jon Dattorro
Paperback: $26.01
Ships in 3-5 business days
Convex Analysis is the calculus of inequalities while Convex Optimization is its application. Analysis is inherently the domain of the mathematician while Optimization belongs to the engineer. In... More > layman’s terms, the mathematical science of Optimization is the study of how to make a good choice when confronted with conflicting requirements. The qualifier Convex means: when an optimal solution is found, then it is guaranteed to be a best solution; there is no better choice. As any Convex Optimization problem has geometric interpretation, this book is about Convex Optimization, convex geometry (with particular attention to distance geometry), and nonconvex, combinatorial, and geometrical problems that can be relaxed or transformed into convex problems. A virtual flood of new applications follows by epiphany that many problems, presumed nonconvex, can be so transformed. black & white paperback< Less
Convex Optimization & Euclidean Distance Geometry By Jon Dattorro
Paperback: $99.99
Ships in 3-5 business days
Convex Analysis is the calculus of inequalities while Convex Optimization is its application. Analysis is inherently the domain of the mathematician while Optimization belongs to the engineer. In... More > layman’s terms, the mathematical science of Optimization is the study of how to make a good choice when confronted with conflicting requirements. The qualifier Convex means: when an optimal solution is found, then it is guaranteed to be a best solution; there is no better choice. As any Convex Optimization problem has geometric interpretation, this book is about Convex Optimization, convex geometry (with particular attention to distance geometry), and nonconvex, combinatorial, and geometrical problems that can be relaxed or transformed into convex problems. A virtual flood of new applications follows by epiphany that many problems, presumed nonconvex, can be so transformed. Revised & Enlarged International Paperback Edition III< Less
Approximation and Solution Schemes for Stochastic Dynamic Optimization Problems By Lisa A. Korf
eBook (PDF): $6.99
Download immediately.
Optimization and control problems often need to be formulated in a way that takes the uncertainty of the future into account in order to accurately reflect a "good" decision that can stand... More > up to a variety of possible future outcomes. One way of including uncertainty in such problems treats the uncertain parameters as a random vector with an underlying probability distribution. Doing this creates a stochastic programming model which is inherently infinite dimensional, or at best extremely large, in particular when many time stages are present. In order to solve such problems, a good approximation framework is needed that encompasses various approaches such as sampling and analytical methods for various problem classes. Complementing this should be a development of solution procedures that exploit a problem's structure, for example taking advantage of convexity and decomposability wherever possible. This dissertation addresses these key issues in four parts.< Less
Approximation and Solution Schemes for Stochastic Dynamic Optimization Problems By Lisa A. Korf
Paperback: $14.99
Ships in 3-5 business days
Optimization and control problems often need to be formulated in a way that takes the uncertainty of the future into account in order to accurately reflect a "good" decision that can stand... More > up to a variety of possible future outcomes. One way of including uncertainty in such problems treats the uncertain parameters as a random vector with an underlying probability distribution. Doing this creates a stochastic programming model which is inherently infinite dimensional, or at best extremely large, in particular when many time stages are present. In order to solve such problems, a good approximation framework is needed that encompasses various approaches such as sampling and analytical methods for various problem classes. Complementing this should be a development of solution procedures that exploit a problem's structure, for example taking advantage of convexity and decomposability wherever possible. This dissertation addresses these key issues in four parts.< Less
Learning algorithms and statistical software, with applications to bioinformatics By Toby Dylan Hocking
Paperback: $50.00
Ships in 3-5 business days
Toby Dylan Hocking's PhD thesis is divided into two parts: learning algorithms and statistical software. Algorithms for segmentation, clustering, and model selection are discussed in the first part,... More > and the second part explains how to implement several statistical software packages in the R language. The content, which includes 47 color figures, should be appreciated by a reader with a background in statistics, data analysis, machine learning, or programming.< Less