滚动时域估计的基本策略就是将状态估计问题化为一个二次规划问题。
The basic strategy of MHE is to reformulate the estimation problem asa quadratic program using a moving estimation window.
该算法能针对在样本有限的情况下，采用结构风险最小化准则，把学习问题转化为一个二次规划问题来获得最优解。
The method can transfer the learning problem into a second planning to acquire the optimal solution according to the principle of structure risk minimum under limited samples situation.
建立非线性等式和不等式约束规划问题的一个序列二次规划(sqp)型算法。
An algorithm of successive quadratic programming (SQP) type is presented to program problems with nonlinear equality and inequality constraints.
针对凸约束非凸二次规划问题，给出了一个分枝定界方法。
In this paper, a branch-and-bound method is proposed for non-convex quadratic programming problems with convex constrains.
建立了道路识别问题的数学模型并将上述问题与一个不等式约束的正定二次规划问题相联系。
A mathematic model of road identification problem is provided and the problem is related with a positive definite quadratic programming problem with inequality constraints.
现有的大多数分类问题都能转化成一个正定二次规划问题的求解。
Most existed classification problems can be converted into a positive definite quadratic program.
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)。
In this paper, a modified sequential quadratic program (SQP) for inequality constrained optimization problems is presented.
该模型通过引入权系数，使多目标问题转化为一个二次目标、线性约束的二次规划问题。
The weight coefficient was introduced into the model, and the multi-objective problem was changed into a quadratic-programming problem.
考虑了广义二次规划问题，基于其鞍点的充要条件，提出了求解它的一个神经网络。
This paper considers the extended quadratic programming problem. Based on the necessary and sufficient conditions for a saddle point, a neural network for solving it is proposed.
采用椭球剖分策略剖分可行域为小的椭球，用投影次梯度算法解松弛二次规划问题的拉格朗日对偶问题，从而获得原问题的一个下界。
A projection subgradient algorithm for the Lagrangian dual problem of the relaxed quadratic problem is employed to general lower bounds of the optimal value for the original problem.
采用椭球剖分策略剖分可行域为小的椭球，用投影次梯度算法解松弛二次规划问题的拉格朗日对偶问题，从而获得原问题的一个下界。
A projection subgradient algorithm for the Lagrangian dual problem of the relaxed quadratic problem is employed to general lower bounds of the optimal value for the original problem.
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