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A Modified Active-Set Algorithm for Quadratic Programming
Author: YiYingHua
Tutor: YeXiangQi
School: Jiangxi Normal University
Course: Applied Mathematics
Keywords: Quadratic Programming Active Set Algorithm Projection Gradient Method Dimensionality reduction method
CLC: O221.2
Type: Master's thesis
Year: 2008
Downloads: 115
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Abstract
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Quadratic programming is the most fundamental and simple nonlinear programming problem . Because of its specificity , and the solving of nonlinear programming problem can be approached by secondary conversion to solving a series of quadratic programming sub-problems , it has a very important significance for quadratic programming algorithm . The theme of this paper is the study of quadratic programming algorithm . The Chapter prior knowledge introduces some basic concepts related to nonlinear programming . Such as gradient , Hessian matrix convex sets , convex function , the Taylor expansion , down direction and feasible direction . Second chapter is the improved index set quadratic programming algorithm , where improvements include two aspects, one is constrained quadratic programming sub-problems in solving the equation , the introduction of a dimensionality reduction algorithm , this dimension reduction algorithm a new approach to solving the equation from Professor Li Zemin proposed constrained nonlinear programming problem ; on the other hand been improved search direction , namely (?) ~ k ≠ x ~ k , x ~ k point gradient projection direction as the direction of the search . The improvement in these two areas , the ultimate goal is to reduce the number of iterations to improve operational efficiency , the instance of the third chapter of the convergence proof , fully illustrate the effectiveness and superiority of the improved algorithm .
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CLC: > Mathematical sciences and chemical > Mathematics > Operations Research > Planning Theory ( mathematical programming) > Nonlinear Programming
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