A mathematical view of interiorpoint methods in convex optimization
by James Renegar

Persian Title: نمایش ریاضی از روش داخلی نقطه در بهینه سازی محدب

Summary and Info
This compact book, through the simplifying perspective it presents, will take a reader who knows little of interiorpoint methods to within sight of the research frontier, developing key ideas that were over a decade in the making by numerous interiorpoint method researchers. It aims at developing a thorough understanding of the most general theory for interiorpoint methods, a class of algorithms for convex optimization problems. The study of these algorithms has dominated the continuous optimization literature for nearly 15 years. In that time, the theory has matured tremendously, but much of the literature is difficult to understand, even for specialists. By focusing only on essential elements of the theory and emphasizing the underlying geometry, A Mathematical View of InteriorPoint Methods in Convex Optimization makes the theory accessible to a wide audience, allowing them to quickly develop a fundamental understanding of the material. The author begins with a general presentation of material pertinent to continuous optimization theory, phrased so as to be readily applicable in developing interiorpoint method theory. This presentation is written in such a way that even motivated Ph.D. students who have never had a course on continuous optimization can gain sufficient intuition to fully understand the deeper theory that follows. Renegar continues by developing the basic interiorpoint method theory, with emphasis on motivation and intuition. In the final chapter, he focuses on the relations between interiorpoint methods and duality theory, including a selfcontained introduction to classical duality theory for conic programming; an exploration of symmetric cones; and the development of the general theory of primaldual algorithms for solving conic programming optimization problems. Rather than attempting to be encyclopedic, A Mathematical View of InteriorPoint Methods in Convex Optimization gives the reader a solid understanding of the core concepts and relations, the kind of understanding that stays with a reader long after the book is finished.
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