Abstract

Quadratic programming (QP) problems, namely minimization of quadratic functions under linear constraints, have been under extensive research since the early days of mathematical programming. In particular, if the objective function is convex, then a large scale problem can be solved by several simplex type algorithms(Beale (1959), Cottle, Dantzig (1968), Wolfe (1959)) or by interior point algorithms (Kojima et al (1991)) ). These algorithms have been successfully applied to a number of practical problems in portfolio analysis (Pang (1980), Takehara (1993), etc.. Also it has been used as a sub-procedure for solving general convex minimization problems (Boggs and Tolle (1995)).KeywordsProgramming ProblemFeasible RegionQuadratic Programming ProblemQuadratic Assignment ProblemOuter ApproximationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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