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Convex Optimization
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.
House documents
None
Introduction to Applied Linear Algebra
A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.
The Kuyper Center Review
Some religious traditions -- such as Lutheran, Wesleyan, and Eastern Orthodox -- have aesthetically rich resources on which to draw for the renewal of arts in everyday life. In contrast, Calvinism has generally been suspicious of the arts. The essays in this volume attempt to explore new avenues of thought about Calvinism's relation to the arts. Part historical, part theological, and part practical, they offer a wide-ranging exploration of neo-Calvinism's relationship to the arts, both at a general level and in connection with specific art forms. Overall they suggest that the neo-Calvinism espoused by Abraham Kuyper can and should make more of the arts than the traditional view of Reformed Christianity might be thought to allow. Contributors: Clifford B. Anderson John Barber James D. Bratt Michael Brutigam Janet Danielson Neal DeRoo John De Soto James Eglinton Matthew Kaemingk Jennifer Wang William Baltmanis Whitney Albert M. Wolters
Restless
It is 1939. Eva Delectorskaya is a beautiful 28-year-old Russian émigrée living in Paris. As war breaks out she is recruited for the British Secret Service by Lucas Romer, a mysterious Englishman, and under his tutelage she learns to become the perfect spy, to mask her emotions and trust no one, including those she loves most. Since the war, Eva has carefully rebuilt her life as a typically English wife and mother. But once a spy, always a spy. Now she must complete one final assignment, and this time Eva can't do it alone: she needs her daughter's help.
Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers
Surveys the theory and history of the alternating direction method of multipliers, and discusses its applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others.
Boyd's Blue Book
None
Linear Matrix Inequalities in System and Control Theory
In this book the authors reduce a wide variety of problems arising in system and control theory to a handful of convex and quasiconvex optimization problems that involve linear matrix inequalities. These optimization problems can be solved using recently developed numerical algorithms that not only are polynomial-time but also work very well in practice; the reduction therefore can be considered a solution to the original problems. This book opens up an important new research area in which convex optimization is combined with system and control theory, resulting in the solution of a large number of previously unsolved problems.
