Boyd, Stephen P., Vandenberghe, Lieven, Boyd, Stephen  | Cambridge University Press

100,000원  | 20080407  | 9780521833783

Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems. Duality and approximation techniques are then covered, as are statistical estimation techniques. Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods. The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics.

Guo, Lei  | Springer

202,100원  | 20210101  | 9781849960298

A recent development in SDC-related problems is the establishment of intelligent SDC models and the intensive use of LMI-based convex optimization methods. Within this theoretical framework, control parameter determination can be designed and stability and robustness of closed-loop systems can be analyzed. This book describes the new framework of SDC system design and provides a comprehensive description of the modelling of controller design tools and their real-time implementation.

Nisheeth K. Vishnoi  | Cambridge University Press

84,520원  | 20211007  | 9781108741774

Algorithms for Convex Optimization are the workhorses of data-driven, technological advancements in machine learning and artificial intelligence. This concise, modern guide to deriving these algorithms is self-contained and accessible to advanced students, practitioners, and researchers in computer science, operations research, and data science.

Moklyachuk, Mikhail  | Wiley-Iste

196,380원  | 20210303  | 9781786306838

Radu Ioan Bot  | Springer

202,100원  | 20210101  | 9783642048999

This book presents new achievements and results in the theory of conjugate duality for convex optimization problems. The perturbation approach for attaching a dual problem to a primal one makes the object of a preliminary chapter, where also an overview of the classical generalized interior point regularity conditions is given.

Hazan, Elad  | MIT Press

115,670원  | 20220920  | 9780262046985

New edition of a graduate-level textbook on that focuses on online convex optimization, a machine learning framework that views optimization as a process. In many practical applications, the environment is so complex that it is not feasible to lay out a comprehensive theoretical model and use classical algorithmic theory and/or mathematical optimization. Introduction to Online Convex Optimization presents a robust machine learning approach that contains elements of mathematical optimization, game theory, and learning theory: an optimization method that learns from experience as more aspects of the problem are observed. This view of optimization as a process has led to some spectacular successes in modeling and systems that have become part of our daily lives. Based on the "Theoretical Machine Learning" course taught by the author at Princeton University, the second edition of this widely used graduate level text features: Thoroughly updated material throughout New chapters on boosting, adaptive regret, and approachability and expanded exposition on optimization Examples of applications, including prediction from expert advice, portfolio selection, matrix completion and recommendation systems, SVM training, offered throughout Exercises that guide students in completing parts of proofs

Meboo Publishing  | Meboo Publishing

73,290원  | 20191029  | 9780578161402

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 layman's terms, the mathematical science of Optimization is a 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.

 | Springer Nature B.V.

73,480원  | 20140114  | 9781441988546

It was in the middle of the 1980s, when the seminal paper by Kar­ markar opened a new epoch in nonlinear optimization. The importance of this paper, containing a new polynomial-time algorithm for linear op­ timization problems, was not only in its complexity bound. At that time, the most surprising feature of this algorithm was that the theoretical pre­ diction of its high efficiency was supported by excellent computational results.

Dutta, Joydeep  | CRC Press

456,630원  | 20111017  | 9781439868225

Covering the current state of the art, this book explores an important and central issue in convex optimization: optimality conditions. Although many results presented in the chapters can also be proved in infinite dimensions, the text focuses on finite dimensions to allow for much deeper results and a better understanding of the structures involved in a convex optimization problem. The authors include examples wherever needed, details of major results, and proofs of the main results.

Michael H. Veatch  | Wiley

119,430원  | 20210127  | 9781119664048

Bubeck, Sebastien  | Now Publishers

174,560원  | 20151112  | 9781601988607

This monograph presents the main complexity theorems in convex optimization and their corresponding algorithms. It begins with the fundamental theory of black-box optimization and proceeds to guide the reader through recent advances in structural optimization and stochastic optimization. The presentation of black-box optimization, strongly influenced by the seminal book by Nesterov, includes the analysis of cutting plane methods, as well as (accelerated) gradient descent schemes.

 | Meboo Publishing

183,730원  | 20150216  | 9780615193687

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 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.

Taylor, Joshua Adam  | Cambridge University Press

194,770원  | 20150406  | 9781107076877

This mathematically rigorous guide to convex optimization for power systems engineering includes convex models for a variety of real-world applications, and a selection of problems and practical examples. An invaluable resource for students and researchers from industry and academia in power systems, optimization, and control.

 | Lightning Source Inc

183,750원  | 20060730  | 9781847280640

Optimization is the science of making a best choice in the face of conflicting requirements. Any convex optimization problem has geometric interpretation. If a given optimization problem can be transformed to a convex equivalent, then this interpretive benefit is acquired. That is a powerful attraction: the ability to visualize geometry of an optimization problem. Conversely, recent advances in geometry hold convex optimization within their proofs' core.

Boyd, Stephen, Busseti, Enzo, Diamond, Steven  | Now Publishers

119,430원  | 20170728  | 9781680833287

Multi-Period Trading via Convex Optimization considers a basic model of multi-period trading, which can be used to evaluate the performance of a trading strategy. It describes a framework for single-period optimization, where the trades in each period are found by solving a convex optimization problem that trades o? expected return, risk, transaction cost and holding cost such as the borrowing cost for shorting assets.