Convex optimization problem solve

A convex optimization problem is a problem where all of the constraints are convex functions, and the objective is a convex function if minimizing, or a concave function if maximizing. Linear functions are convex, so linear programming problems are convex problems.

Convex optimization problems can also be solved by the following contemporary methods: Bundle methods (Wolfe, Lemaréchal, Kiwiel), and Subgradient projection methods (Polyak), Interior-point methods, which make use of self-concordant barrier functions and self-regular barrier functions.