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Line 16: If ''Q'' is [[positive-definite matrix\|positive definite]], then ''f''('''x''') is a [[convex function]] and constraints are [[linear]] functions. We know from optimization theory that for point '''x''' to be an optimum point it is necessary and sufficient that '''x''' is a [[Karush-Kuhn-Tucker]] (KKT) point. If there are only equality constraints, then the QP can be solved by a [[linear system]]. Otherwise, the most common method of solving a QP is an [[interior point method]~~], such as [http://www.princeton.edu/~rvdb/loqo LOQO~~]. [[Active set]] methods are also commonly used, as well as [[Conjugate gradient method]] with projection. ==Complexity==

Quadratic programming: Difference between revisions