This textbook provides a thorough introduction to spectrahedra, which are the solution sets to linear matrix inequalities, emerging in convex and polynomial optimization, analysis, combinatorics, and algebraic geometry. Including a wealth of examples and exercises, this textbook guides the reader in helping to determine the convex sets that can be represented and approximated as spectrahedra and their shadows (projections). Several general results obtained in the last 15 years by a variety of different methods are presented in the book, along with the necessary background from algebra and geometry.

Autorentext

Tim Netzer is a professor of applied algebra at the University of Innsbruck. He received his PhD in 2008 from the University of Konstanz. His research is in real algebra and geometry, with connections to optimization, functional analysis, and quantum information theory. He has worked at the Universities of Saskatchewan, Leipzig, and Dresden. Daniel Plaumann is a professor of algebra and its applications at Dortmund University. He received his PhD in 2008 from the University of Konstanz. His research is in real and classical algebraic geometry. He has been a visiting scholar at the University of California, Berkeley, and at Nanyang Technological University, Singapore.