And then, we present an approximation method for solving this probabilistic constrained stochastic programming, and prove certain convergence of the method under some conditions.
随后我们提出了求解这类概率约束随机规划的一种近似算法，并在一定的条件下证明了算法的收敛性。
This article deals with the basic methods for solving unconstrained and constrained nonlinear problems. It describes also the linear approximation methods of nonlinear programming.
本文阐明无约束和有约束非线性问题的基本解法，并说明非线性规划的线性近似方法。
The improved approximate expressions are proposed to create the sequential approximation problems which are solved with the constrained variable metric method.
用该近似函数形成结构优化原问题的序列近似问题，再用约束变尺度法解近似问题。
To obtain a stable solution, in our method, successive approximation process is constrained by prior histogram and laplacian regularization.
为了获得稳定而满意的解，我们采用直方图约束下的正则化方法对连续近似迭代进行约束。
In this paper, a smooth approximation-BFGS method for solving inequality constrained nonlinear programming is presented.
本文对不等式约束非线性规划提出一种光滑逼近- BFGS法。
Then the result is interpolated into the next finer level of approximation image as a initial contour, and evolved with edge based and inter scales shape constrained ACM.
然后通过插值将结果向下一尺度低频图像传递，并利用尺度间形状约束和边界约束复合acm进一步细化曲线，使其符合局部图像特征，如此逐层重复直至原始图像。
Then the result is interpolated into the next finer level of approximation image as a initial contour, and evolved with edge based and inter scales shape constrained ACM.
然后通过插值将结果向下一尺度低频图像传递，并利用尺度间形状约束和边界约束复合acm进一步细化曲线，使其符合局部图像特征，如此逐层重复直至原始图像。
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