| 1. | On a class of quadratic programming problem with equality constraints
一类带有等式约束的二次规划问题 |
| 2. | Fixed iterative method for solving the inequality constrained quadratic programming problem
不等式约束二次规划的不动点迭代 |
| 3. | 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 |
| 4. | A global convergence inner - point style algorithm for generic quadratic programming problem
二次规划问题的一个全局收敛的内点型算法 |
| 5. | Training svm can be formulated into a quadratic programming problem . for large learning tasks with many training examples , off - the - shelf optimization techniques quickly become intractable in their memory and time requirements . thus , many efficient techniques have been developed
训练svm的本质是解决一个二次规划问题，在实际应用中，如果用于训练的样本数很大，标准的二次型优化技术就很难应用。 |
| 6. | 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。 |
| 7. | A program of shape optimization for two - dimensional continuum structures , which is carried out on msc . patran & nastran panel , is described in this thesis , according to a two - phase control theory presented by professor sui yunkang , the optimization is treated as a sequential quadratic programming problem
本文根据隋允康教授提出的二级控制理论，将二维连续体结构的形状优化问题处理成序列二次规划问题进行了优化。 |
| 8. | For nonlinear l1 problem based on the conditions for optimality of the nonlinear l1 problem in [ 1 ] , we first discuss the descent direction of the objective function f ( x ) in theory , further more , we study the relation between the optimal solution of nonlinear l1 problem and the optimal solution of some kind of quadratic programming problem with box constrains . hence , we construct a descent algorithm for nonlinear l1 problem and prove the convergence of the algorithm
在文[ 1 ]所给的最优性条件的基础上，对非线性l _ 1问题从理论上研究了f ( x )的下降方向、最优解与某种框式约束最小二乘问题的最优解之间的关系，进而构造了一个非线性l _ 1问题的下降算法，并证明了该算法的收敛性。 |
| 9. | When solving the problems , we use the support vector regression ( svr ) . first assuming the formula of function , then according to the differential and boundary conditions we transform the original problem to the quadratic programming problem . finally , use the learning algorithm of svr to decide the parameters
只要事先假设出所求函数的表达式，然后根据已知的微分关系和边界条件对待求函数进行约束将原问题转化为二次规划问题，再采用支持向量机回归算法对样本进行学习即可求出参数，确定待定函数的关系式。 |
| 10. | The separating plane with maximal margin is the optimal separating hyperplane which has good generation ability . to find a optimal separating hyperplane leads to a quadratic programming problem which is a special optimization problem . after optimization all vectors are evaluated a weight . the vector whose weight is not zero is called support vector
而寻找最优分类超平面需要解决二次规划这样一个特殊的优化问题，通过优化，每个向量(样本)被赋予一个权值，权值不为0的向量称为支持向量，分类超平面是由支持向量构造的。 |
