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SeeComplexity theory: Quadratic programmingellipsoid methodĪdler, I., and Shamir, R.: ‘A randomization scheme for speeding up algorithms for linear and convex quadratic programming problems with a high constraints-to-variables ratio’, Math.

SeeComplexity theory: Quadratic programmingbox constraints.

SeeComplexity theory: Quadratic programmingsimplicial constraints.

SeeComplexity theory: Quadratic programming Cutting-stock problem Simplicial pivoting algorithms for integer programmingknapsack problem.

SeeComplexity theory: Quadratic programmingstrongly polynomial time.

SeeComplexity theory: Quadratic programmingtrust region problem.

SeeComplexity theory: Quadratic programminglocal minimization.

SeeComplexity theory: Quadratic programming Frank–Wolfe algorithm Linear complementarity problem Operations research and financial markets Portfolio selection: Markowitz mean-variance model Quadratic knapsack Quadratic programming over an ellipsoid Quadratic programming with bound constraints Replicator dynamics in combinatorial optimization Sensitivity and stability in NLP: Approximation Successive quadratic programming: Solution by active sets and interior point methodsquadratic programming.In particular, there is no local minimizer other. Convex quadratic programming inherits all the desirable attributes of the general convex programming.