convex constrained optimization 凸约束优化 ; 凸约束的非线性规划问题
nonlinear convex constrained optimization 凸约束的非线性规划问题
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。
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