Going quadratic: applying advanced techniques from linear programming to convex quadratic programming
Going quadratic: applying advanced techniques from linear programming to convex quadratic programming
Convex quadratic programming is the most natural extension to linear programming. In this dissertation, a number of established techniques for linear programming are extended to the quadratic case and checked for their merit in practical use. This includes the application of structure detection techniques to find suitable decompositions in general convex quadratic programming instances. A theoretical analysis of the use of parallelism within an active set solver is given. Two pricing techniques developed for the simplex algorithm for linear programming are implemented for quadratic programming and their performance analyzed over a large set of test instances.
- University of Edinburgh United Kingdom
convex quadratic programming, structure detection techniques, linear programming, 004
convex quadratic programming, structure detection techniques, linear programming, 004
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