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## A New Heuristic for the Convex Quadratic Programming Problem (2015)

### Citations

47 | Algorithms for bound constrained quadratic programming problems - Moré, Toraldo - 1989 |

37 | A repository of convex quadratic programming problems, Optimization Methods and Software
- Meszaros
- 1999
(Show Context)
Citation Context ... = = = = = =sThisssolution is once again optimal as all complementary slackness conditions are satisfied.s5. Computational ExperimentssA set of convex quadratic programming test problems are given in =-=[14]-=-. All these test problems were used instesting the proposed approach. The objective of the computational experiments was:s1) To determine that the LP optimal solution is also optimal to the given QP.s... |

28 |
J.F.: Operations Research Models and Methods
- Jensen, Bard
- 2002
(Show Context)
Citation Context ...o a minimization and vice versa.sWhen the function ( )f Xsis strictly convex for all points in the convex region then the quadratic problemshas a unique local minimum which is also the global minimum =-=[11]-=-.s2.2. Karush-Kuhn-Tucker ConditionssThe convex quadratic programming problem has special features that we can capitalize on when solving. Allsconstraints are linear and the only nonlinear expression ... |

15 | Introduction to Global Optimization (Nonconvex Optimization and Its Applications - Horst - 2002 |

14 | Globally solving box-constrained nonconvex quadratic programs with semidefinite-based finite branch-and-bound - Burer, Vandenbussche - 2009 |

14 | Interior point methods 25 years later - Gondzio |

8 |
Operations Research Applications and Algorithms. 4th Edition
- Winston
- 2004
(Show Context)
Citation Context ...s1 1 2 2 1 1 2 2 0s s y x y xλ λ= = = =s(26)s4.2. Two More ExamplessTwo more examples are solved to illustrate how the large constants are selected. Example 2 is taken from [12]sand example 3 is from =-=[13]-=-.sExample 2 from [12]sMinimize ( ) ( )2 21 21 2.5x x− + −sSubject to:s1 2 1 2 1 2 1 22 2, 2 6, 2 2, , 0x x x x x x x x− + ≤ + ≤ − ≤ ≥s(27)sThe linear formulation of (27) becomes as given in (28).sMaxi... |

3 |
A numerical solution method to interval quadratic programming
- Liu, Wang
(Show Context)
Citation Context ...finite impulse design; see [1]-[3]. Some of the methods for solving the convex quadratic problem are active set,sinterior point, branch and bound, gradient projection, and Lagrangian methods, see [4]-=-=[9]-=- for more informationson these methods.sIn this paper we present a new heuristic to linearise the convex quadratic programming problem. The usualsKarush-Kuhn-Tucker conditions are still used but in th... |

2 |
Quadratic Programming Applications
- McCarl, Moskowitz, et al.
- 1977
(Show Context)
Citation Context ...uadratic programming (QP) problem. The applicationssinclude portfolio analysis, structural analysis, discrete-time stabilisation, optimal control, economic dispatch andsfinite impulse design; see [1]-=-=[3]-=-. Some of the methods for solving the convex quadratic problem are active set,sinterior point, branch and bound, gradient projection, and Lagrangian methods, see [4]-[9] for more informationson these ... |

2 | A Regularized Active-Set Method for Sparse Convex Quadratic Programming
- Maes
- 2010
(Show Context)
Citation Context ...tionalsexperiments have been carried out and the objective of the computational experiments was to determine CPUstimes of the:s1) Proposed heuristic;s2) Regularised Active Set Method Mae and Saunders =-=[10]-=-;s3) Primal-Dual Interior Point Algorithm.sIt may be noted that the proposed method is suitable only if the quadratic programming problem satisfiessconditions (1) to (5) mentioned in Section 2.1.s2 Ma... |

1 |
Applications of Quadratic Programming
- Gupta
- 1995
(Show Context)
Citation Context ...ex quadratic programming (QP) problem. The applicationssinclude portfolio analysis, structural analysis, discrete-time stabilisation, optimal control, economic dispatch andsfinite impulse design; see =-=[1]-=--[3]. Some of the methods for solving the convex quadratic problem are active set,sinterior point, branch and bound, gradient projection, and Lagrangian methods, see [4]-[9] for more informationson th... |

1 |
A Finite Branch and Bound Algorithm for Non-Convex Quadratic Programs with Semidefinite Relaxations
- Burer, Vandenbussche
- 2008
(Show Context)
Citation Context ...andsfinite impulse design; see [1]-[3]. Some of the methods for solving the convex quadratic problem are active set,sinterior point, branch and bound, gradient projection, and Lagrangian methods, see =-=[4]-=--[9] for more informationson these methods.sIn this paper we present a new heuristic to linearise the convex quadratic programming problem. The usualsKarush-Kuhn-Tucker conditions are still used but i... |

1 | Solution Methods for Quadratic Optimization - Freund - 2002 |

1 |
Unit 8: Quadratic Programming Active set
- Lee
- 2011
(Show Context)
Citation Context ...satisfied as given in (26).s1 1 2 2 1 1 2 2 0s s y x y xλ λ= = = =s(26)s4.2. Two More ExamplessTwo more examples are solved to illustrate how the large constants are selected. Example 2 is taken from =-=[12]-=-sand example 3 is from [13].sExample 2 from [12]sMinimize ( ) ( )2 21 21 2.5x x− + −sSubject to:s1 2 1 2 1 2 1 22 2, 2 6, 2 2, , 0x x x x x x x x− + ≤ + ≤ − ≤ ≥s(27)sThe linear formulation of (27) bec... |