By utilizing the linear underestimation of objective and constraint functions, a linear relaxation method is proposed for finding global solution of SGP. 本文利用目标函数和约束函数的线性下界估计,提出一种求(SGP)问题全局解的线性松弛(LR)方法。
A Global Optimization Algorithm Using Linear Relaxation(LR) 一类全局优化问题的线性化方法
For large scale problems, get lower bound by linear relaxation, and verification the lower bound quality get by lagrangian relaxation. 对于大规模问题,利用线性松弛(LR)求得下界,然后验证拉格朗日松弛算法求得的下界。
In addition, based on linear programming relaxation theory, a method to compute the theoretical lower bound of MALMM's solution is introduced. 利用线性规划松弛理论,设计一种求MALMM解理论下限的方法。
A model of linear strain relaxation is proposed to obtain the interatomic longitudinal spacings of the strained superlattice. 在线性驰豫应变假设下给出了应变超晶格纵向原子间距的几何模型。