DPCM

matlab小程序，用来求趋势，很简单。主要是二次函数拟合趋势。(matlab small program used to find trends, very simple. The main trend is to fit a quadratic function.)

说明：  在极坐标下计算电力系统网络潮流以及加设FACTS（UPFC）原件后的潮流调整(Calculated in polar coordinates, and the trend of power system network to install FACTS (UPFC) original trend after adjustment)

matlab中解决高斯滤波的问题，包括高通中通和低通滤波器(to solve the problem of )

svm 的参数优化，利用交叉验证法选择最优参数c g，最终提高训练集的分类准确率,更好的提高分类器性能(Svm parameter optimization, the use of cross-validation method to the optimal parameter c g, and ultimately improve the training set classification accuracy，better improve the classifier performance)

求解椭圆型，双曲型和抛物型偏微分方程的matlab代码(Solve the elliptic, hyperbolic and parabolic partial differential equations matlab code)

in this package are the programs needed to solve any problems boundary value differential.

pulse with modulation for sinusidal wave making

A Summary of Implicit Snake Formulations Coupled Multi-shape Model and Mutual Information for Medical Image Segmentation (2d snakeA Summary of Implicit Snake Formulations Coupled Multi-shape Model and Mutual Information for Medical Image Segmentation )

matlab采用半色调技术对图像进行显示：用模板近似表示10个灰度级，每一个灰度级用3*3 的模板来表示，并对如下图所示的256*256的渐变图像进行半色调输出的检验。(matlab using halftoning image display: template approximated 10 gray levels, a gray level of each template used to represent the 3* 3, and the gradation of the image as shown in Figure 256* 256 halftone inspection of the output.)

本书是The IMA Volumes in Mathematics and its Applications系列的一本，由Springer-Verlag发表，用于交流每年数学规划算法方面的新成果，希望对于广泛的科学团体有帮助。 许多工程，运筹，和科学应用包括离散或连续的决策变量，以及这些决策变量的非线性关系。这种 Mixed-integer nonlinear programming （MINLP，混合整数非线性规划），兼具来自非线性问题的非凸函数优化，以及来自整数的离散优化，这两方面的挑战。这种问题是优化问题里面建模最灵活的类型之一，但是因为它范围极广，在最一般的情况下，它是几乎无法解的。然而，日渐壮大的研究者和程序员队伍，包括化学工程，运筹研究者，工业工程师，机械工程师，经济学家，统计学家，计算机科学家，运营经理，和数学程序员，都对求解大规模MINLP问题产生兴趣。(Many engineering, operations, and scientific applications include a mixture of discrete and continuous decision variables and nonlinear relationships involving the decision variables that have a pronounced effect on the set of feasible and optimal solutions. Mixed-integer nonlinear programming (MINLP) problems combine the numerical difficulties of handling nonlinear functions with the challenge of optimizing in the context of nonconvex functions and discrete variables. MINLP is one of the most flexible modeling paradigms available for optimization but because its scope is so broad, in the most general cases it is hopelessly intractable. Nonetheless, an expanding body of researchers and practitioners ― including chemical engineers, operations researchers, industrial engineers, mechanical engineers, economists, statisticians, computer scientists, operations managers, and mathematical programmers ― are interested in solving large-scale MINLP instances.)