Because 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 dual …

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. …

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 …

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?

Convex Optimization is a mathematically rigorous and well-studied field. In linear programming a whole host of tractable methods give your global optimums in lightning fast times. Quadratic …

Jan 23, 2022 · In particular, why is "Convexity" so important, such that it (historically) made us interested in classifying functions as either Convex or Non-Convex? I found Roman J. …

Apr 9, 2020 · This is the convex problem where the dual problem has no feasible solution and KKT conditions have no solution but the primal problem is simple to solve. $ {\bf counter …

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 …

Apr 10, 2020 · 8 I am looking for optimization books. Can you suggest some good materials? First, I started with Convex Optimization by Stephen Boyd & Lieven Vandenberghe, but I don't …