An interior point algorithm for convex quadratic programming problem with box constraints 框式约束凸二次规划问题的内点算法
In order to improve the efficiency of the algorithm, we not only correct some defects of the primal-dual interior point algorithm in [ 4 ], but also give a modified primal-dual interior point algorithm for convex quadratic programming problem with box constraints 为提高算法的有效性,对文[4]所给的原始-对偶内点算法理论上的某些缺陷加以更正,并给出框式约束凸二次规划问题的一个修正原始-对偶内点算法。
Based on the statistical learning theory and optimization theory, svms have been successfully applied to many fields such as pattern recognition, regression and etc . training an svm amounts to solving a convex quadratic programming problem . in this paper we do some researches on svms by the optimization theory and method 它将机器学习问题转化为求解最优化问题,并应用最优化理论构造算法来解决问题,本文主要是从最优化理论和算法的角度对支持向量机中的最优化问题进行研究。