Many scientific, engineering and economic areas involve the optimization of complex, nonlinear and possibly non-convex problems.

科学领域，工程领域和经济领域都涉及到很多复杂的、非线性的甚至非凸形式的最优化问题。

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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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Due to the non-convex of the prior function and hyper-parameters, we use the dynamic posterior simulation rather than the general optimization methods to get reconstruction image.

由于采用的先验函数是非凸的并包含超验参数，一般的优化方法难以处理，采用动态后验模拟的方法可以很好地解决这些问题。

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