Also known as: Convex Optimisation
Convex optimization is the process of minimizing or maximizing a convex objective subject to constraints that preserve convexity. It is important in machine learning and operations research because convex problems can often be solved reliably and efficiently.
The process of using mathematical techniques such as gradient descent to find the minimum of a convex function. A great deal of research in machine learning has focused on formulating various problems as convex optimization problems and in solving those problems more efficiently. For complete details, see Boyd and Vandenberghe, Convex Optimization.