The updating problems of structure models are turned into the optimization with a quadratic constraint.
该方法将模型修正问题转化为一个带二次约束的最优化问题。
In this paper, a modified sequential quadratic program (SQP) for inequality constrained optimization problems is presented.
本文对不等式优化问题提出了一个修正的序列二次规划算法(SQP)。
Sequential quadratic programming (SQP) method is an efficient method for solving smooth constrained optimization problems because of its fast convergence rate.
由于序列二次规划（SQP）算法具有快速收敛速度，所以它是求解光滑约束优化问题的有效方法之一。
In this paper, we are concerned with the sequence quadratic programming (SQP) methods for solving constrained optimization problems.
本文研究求解约束最优化问题的序列二次规划算法(SQP算法)。
The basic idea of these methods is to approximate the optimization problem by a sequence of quadratic minimization problems subject to some trust region.
该类算法的基本思想是通过求解一系列二次函数在信赖域中的极小值点逼近最优化问题的解。
Hessian matrices are used in large-scale optimization problems within Newton-type methods because they are the coefficient of the quadratic term of a local Taylor expansion of a function.
海森矩阵被应用于牛顿法解决的大规模优化问题。混合偏导数和海森矩阵的对称性海森矩阵的混合偏导数是海森矩阵非主对角线上的元素。
Hessian matrices are used in large-scale optimization problems within Newton-type methods because they are the coefficient of the quadratic term of a local Taylor expansion of a function.
海森矩阵被应用于牛顿法解决的大规模优化问题。混合偏导数和海森矩阵的对称性海森矩阵的混合偏导数是海森矩阵非主对角线上的元素。
应用推荐