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4 results for "rank constraint"
Convex Optimization & Euclidean Distance Geometry By Jon Dattorro
Paperback: $99.99 (excl. taxes)
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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 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
Machine Learning (Convex Optimization for Sissies) By Dattorro
Hardcover: $148.32 (excl. taxes)
Ships in 6-8 business days.
Convex Analysis is an emerging calculus of inequalities while Convex Optimization is its application. Analysis is 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 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.< Less
Convex Optimization Euclidean Distance Geometry 2e By Dattorro
Paperback: $43.29 (excl. taxes)
Ships in 3-5 business days
Convex Analysis is an emerging calculus of inequalities while Convex Optimization is its application. Analysis is the domain of a 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 faced 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 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. This is a BLACK & WHITE paperback. A hardcover with full color interior, as originally conceived, is available at lulu.com/spotlight/dattorro< Less
Convex Optimization Euclidean Distance Geometry 2e By Dattorro
Hardcover: $149.06 (excl. taxes)
Ships in 6-8 business days.
Convex Analysis is an emerging calculus of inequalities while Convex Optimization is its application. Analysis is the domain of a 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 faced 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 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. Full Color Interior Hardcover< Less