convex quadratic programming造句
例句与造句
- interior point approach for convex quadratic programming
凸二次规划问题的内点算法 - a dual method for solving a class of convex quadratic programs
一类凸二次规划的对偶方法 - predictor-corrector smoothing methods for convex quadratic programs
凸二次规划的预估校正光滑算法 - 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 - a controllable scheduling problem with discrete processing times using convex quadratic programming relaxation
凸二次规划松弛方法研究离散加工时间可控排序问题 - It's difficult to find convex quadratic programming in a sentence. 用convex quadratic programming造句挺难的
- when the hessian is positive definite, the qp subproblem is a strict convex quadratic programming
若qp子问题的hessian阵正定，则它是一个严格凸二次规划问题。 - fifthly, the parameter control methods for solving convex programming, especially linear programming and convex quadratic programming, are discussed and its convergence iv is probed
五是研究了凸规划包括线性规划和凸二次规划的参数控制算法及其收敛性。 - 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。 - the methods employs solving convex quadratic programming directly or solving convex quadratic programming after converting the large-scale problem into many sub-problem or utilizing sophisticated optimization techniques after converting the constrained optimization problem into unconstrained ones
这些方法是通过求解凸二次规划问题或将大规模问题转化成若干子问题再求解凸二次规划问题,或者是转化为无约束最优化问题再利用比较成熟的最优化方法求解。 - the methods employs solving convex quadratic programming directly or solving convex quadratic programming after converting the large-scale problem into many sub-problem or utilizing sophisticated optimization techniques after converting the constrained optimization problem into unconstrained ones
这些方法是通过求解凸二次规划问题或将大规模问题转化成若干子问题再求解凸二次规划问题,或者是转化为无约束最优化问题再利用比较成熟的最优化方法求解。 - this paper applies generalized multipler method to translate convex quadratic programs with equal constraints and non-negative constraints into simple convex quadratic programs with non-negative constraints . the new algorithm is gotten by solving the simple quadratic program . it avoids the computation of inverse matrix and exploits sparsity structure in the matrix of the quadratic form . the results of numerical experiments show the effectiveness of the algorithm on large scale problems
根据广义乘子法的思想，将具有等式约束和非负约束的凸二次规划问题转化为只有非负约束的简单凸二次规划，通过解简单凸二次规划来得到解等式约束和非负约束的凸二次规划新算法，新算法不用求逆矩阵，这样可充分保持矩阵的稀疏性，用来解大规模稀疏问题.数值结果表明：在微机486/33上就能解较大规模的凸二次规划 - this paper applies generalized multipler method to translate convex quadratic programs with equal constraints and non-negative constraints into simple convex quadratic programs with non-negative constraints . the new algorithm is gotten by solving the simple quadratic program . it avoids the computation of inverse matrix and exploits sparsity structure in the matrix of the quadratic form . the results of numerical experiments show the effectiveness of the algorithm on large scale problems
根据广义乘子法的思想，将具有等式约束和非负约束的凸二次规划问题转化为只有非负约束的简单凸二次规划，通过解简单凸二次规划来得到解等式约束和非负约束的凸二次规划新算法，新算法不用求逆矩阵，这样可充分保持矩阵的稀疏性，用来解大规模稀疏问题.数值结果表明：在微机486/33上就能解较大规模的凸二次规划 - basing on the statistical inaming t'heory ( slt ), the thesis discusses the svm problems in linearly separable case, lineariy non-separable case and non-linear separable case, and induces a convex quadratic programming ( qp ) problem with an equation constrain and non-equation constrains . then one program on solving the op problem is proposed
概述了统计学习理论的主要内容，推导了支持向量机方法在文本线性可分、线性不可分和非线性可分情况下实现分类的数学公式，将学习问题转化为一个在等式约束和不等式约束下的凸二次优化问题，总结了求解的过程。 - 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。