Jessica Wilson

Convex Optimization Jessica Wilson

For many general purpose optimization methods, the typical approach is to just try out the method on the problem to be solved. The full benefits of convex optimization, in contrast, only come when the problem is known ahead of time to be convex. Of course, many optimization problems are not convex, and it can be difficult to recognize the ones that are, or to reformulate a problem so that it is convex. Developing a working knowledge of convex optimization can be mathematically demanding, especially for the reader interested primarily in applications. In our experience (mostly with graduate students in electrical engineering and computer science), the investment often pays off well, and sometimes very well. This book is suited for the students who are interested in solving various optimization problems. These concepts are widely used in bioengineering, electrical engineering, machine learning, statistics, economics, finance, scientific computing and computational mathematics and many more. The prerequisites for this course is introduction to linear algebra like introduction to the concepts like matrices, eigenvectors, symmetric matrices; basic calculus and introduction to the optimization like introduction to the concepts of linear programming.