optimization method
31Constraint optimization — In constraint satisfaction, constrained optimization (also called constraint optimization) seeks for a solution maximizing or minimizing a cost function. Contents 1 Definition 2 Solution methods 2.1 Branch and bound …
32Distributed constraint optimization — (DCOP or DisCOP) is the distributed analogue to constraint optimization. A DCOP is a problem in which a group of agents must distributedly choose values for a set of variables such that the cost of a set of constraints over the variables is… …
33Conversion optimization — In internet marketing, conversion optimization, or conversion rate optimization is the method of creating an experience for a website or landing page visitor with the goal of increasing the percentage of visitors that convert into customers. It… …
34Landing page optimization — Internet marketing Display advertising Email marketing E mail marketing software Interactive advertising …
35Nexus (Process integration and optimization) — Nexus Screenshot of Nexus 1.1.05 Developer(s) iChrome …
36Nelder-Mead method — See simplex algorithm for the numerical solution of the linear programming problem. The Nelder Mead method or downhill simplex method or amoeba method is a commonly used nonlinear optimization algorithm. It is due to John Nelder R. Mead (1965)… …
37Interior point method — Interior point methods (also referred to as barrier methods) are a certain class of algorithms to solve linear and nonlinear convex optimization problems. These algorithms have been inspired by Karmarkar s algorithm, developed by Narendra… …
38Subgradient method — Subgradient methods are algorithms for solving convex optimization problems. Originally developed by Naum Z. Shor and others in the 1960s and 1970s, subgradient methods can be used with a non differentiable objective function. When the objective… …
39Cavity method — The Cavity method is a mathematical method due to M. Mezard, Giorgio Parisi and M.A. Virasoro in 1985 to compute properties of ground states in many condensed matter and optimization problems. Initially invented to deal with the Sherrington… …
40Conjugate gradient method — A comparison of the convergence of gradient descent with optimal step size (in green) and conjugate vector (in red) for minimizing a quadratic function associated with a given linear system. Conjugate gradient, assuming exact arithmetic,… …
