In convex programming theory, a constrained optimization problem, by KT conditions, is usually converted into a mixed nonlinear complementarity problem.
在凸规划理论中，通过KT条件，往往将约束最优化问题归结为一个混合互补问题来求解。
In this paper, we develop a trust region algorithm for convex constrained optimization problems.
本文我们考虑求解凸约束优化问题的信赖域方法。
The Chaotic neural network model can be used to solve many multi-dimensioned, discrete, non-convex, nonlinear constrained optimization problems.
基于混沌神经网络模型可以有效地解决高维、离散、非凸的非线性约束优化问题。
This paper presents an interior trust region method for linear constrained LC convex optimization problems.
本文提出一种解线性约束凸规划的数值方法。
This paper presents an interior trust region method for linear constrained LC convex optimization problems.
本文提出一种解线性约束凸规划的数值方法。
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