A non-monotone trust region algorithm with simple quadratic models 基于简单二次函数模型的非单调信赖域算法
Solving Large-scale Quadratic Programming with Simple Quadratic(SQ) Constraint 约束问题求解求解带二次简单约束的大型二次规划问题
Support Vector Machines ( SVM ) adopts structural risk minimization principle and kernel method. It is a simple quadratic programming and has a unique solution. 支持向量机模型采用结构风险极小化原则和核函数方法来构造分类模型,模型比较简单,解具有唯一性。
Consider the stability-robustness of a polynomial-family ( assuming that the coefficients are affine functions of the parameters ). This problem can be converted to an one-parameter simple quadratic programming ( rank 2 ) by the zero-excluding principle. 利用除零原则,多项式族稳定性的判定问题(系数仿射依赖于参数的情形)可以化为单参数秩2简单二次规划问题。
A self-adapt projection contraction method solving large-scale quadratic programming with simple quadratic constraint 求解大规模带二次简单约束的二次规划的显式自调比投影收缩算法