Quadratic programming (QP) involves minimizing or maximizing an objective function subject to bounds, linear equality, and inequality constraints. Example problems include portfolio optimization in finance, power generation optimization for electrical utilities, and design optimization in engineering.
Quadratic programming is the mathematical problem of finding a vector x that minimizes a quadratic function:
Subject to the linear constraints:
The following algorithms are commonly used to solve quadratic programming problems:
For more information about quadratic programming, see Optimization Toolbox.
Optimization in MATLAB: An Introduction to Quadratic Programming 36:35 (Webinar)