For any convex optimization problem with differentiable objective and constraint function, any points that satisfy the KKT conditions are primal and dual optimal and have zero duality gap. …

Because f∗ f ∗ is defined on the dual space, we see already the fundamental role played by the dual space in duality in convex optimization. Given an optimization problem, we don't obtain a …

Jan 12, 2013 · Other books I recommend looking at: Introductory Lectures on Convex Optimization: A Basic Course by Nesterov, Convex Analysis and Nonlinear Optimization by …

I am looking for a C++ library, and I am dealing with convex objective and constraint functions.

Dec 29, 2022 · Conclusion In a convex optimization problem, you can always solve for the KKT conditions (FONC) to achieve a set of minimizer candidates and be sure that all of them are …

I also like of course Rockafellar's old but still fantastic Convex Analysis, Rockafellar and Wets' Variational Analysis. For teaching/selfstudy, Mordukhovich and Nam's An easy path to convex …

The general point of convex problems is that a local minimum is a global minimum. There are all sorts of relaxations and generalizations (or transformations as in posynomials), but generally …

It's well known that linear least squares problems are convex optimization problems. Although this fact is stated in many texts explaining linear least squares I could not find any proof of it. Tha...

Jun 8, 2014 · I am coming across the term: non-convex optimization problem. What exactly is this non-convex structure, and how do I know by only looking at the structure of the problem, I …

Jul 27, 2019 · Just a new guy in optimization. Is it true that all convex optimization problems can be solved in polynomial time using interior-point algorithms?