The notion of convexity underlies a lot of beautiful mathematics. When combined with computation, it gives rise to the area of convex optimization that has had a huge impact on understanding and improving the world we live in. However, convexity does not provide all the answers. Many procedures in statistics, machine learning and nature at large—Bayesian inference, deep learning, protein folding—successfully solve non-convex problems that are NP-hard, i.e., intractable on worst-case instances. Moreover, often nature or humans choose methods that are inefficient in the worst case to solve problems in P.
Can we develop a theory to resolve this mismatch between reality and the predictions of worst-case analysis? Such a theory could identify structure in natural inputs that helps sidestep worst-case complexity.
This blog is dedicated to the idea that optimization methods—whether created by humans or nature, whether convex or nonconvex—are exciting objects of study and, often lead to useful algorithms and insights into nature. This study can be seen as an extension of classical mathematical fields such as dynamical systems and differential equations among others, but with the important addition of the notion of computational efficiency.
We will report on interesting research directions and open problems, and highlight progress that has been made. We will write articles ourselves as well as encourage others to contribute. In doing so, we hope to generate an active dialog between theorists, scientists and practitioners and to motivate a generation of young researchers to work on these important problems.