We establish a Lagrange multiplier theorem for strict efficiency in convex settings and express strict points as saddle points of an appropriate Lagrangian function.
讨论凸多目标最优化问题的严有效解，建立了拉格朗日乘子定理，并把严有效解表示为一个适当的拉格朗日函数的鞍。
Lagrange solution is employed to convert the constrained optimization problem and bisection method is used to reach a fast convergence in searching for the optimize Lagrange multiplier.
该算法利用拉格朗日算法将约束条件下的最优化问题进行转化，并采用对分算法加快搜索最优拉格朗日乘子的收敛速度。
With the Lagrange multiplier method, the minimum distance of the center of a circle and a quadric surface was provided and the tangency condition of curve and surface was given.
利用拉格朗日乘子法求解二次曲线和二次曲面之间的最小距离，给出了曲线与曲面相切的条件。
应用推荐