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)).
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© 1997 Springer Science+Business Media Dordrecht
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Konno, H., Thach, P.T., Tuy, H. (1997). Low Rank Nonconvex Quadratic Programming. In: Optimization on Low Rank Nonconvex Structures. Nonconvex Optimization and Its Applications, vol 15. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4098-4_12
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DOI: https://doi.org/10.1007/978-1-4615-4098-4_12
Publisher Name: Springer, Boston, MA
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