Convex Optimization and Numerical Issues

Abstract

This chapter aims at providing the intuition behind convex optimization algorithms and addresses their effective use with floating-point implementation. It first briefly presents the algorithms, assuming a real semantics. As outlined in Chapter 4, convex conic programming is supported by different methods depending on the cone considered. The most known approach for linear constraints is the simplex method by Dantzig. While having an exponential-time complexity with respect to the number of constraints, the simplex method performs well in general. Another method is the set of interior point methods, initially proposed by Karmarkar and made popular by Nesterov and Nemirovski. They can be characterized as path-following methods in which a sequence of local linear problems are solved, typically by Newton's method. After these algorithms are considered, the chapter discusses approaches to obtain sound results.