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Code Listing by Yi Cao

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Complex step differentiation (CSD) is a superior numerical differentiation approach. Normally, it is more efficient and accurate than finite difference appraoch. This package provides a way to improve the MATLAB lsqnonlin function using the CSD.



Munkres algorithm (also known as Hungarian algorithm) is an efficient algorithm to solve the assignment problem in polynomial-time. The algorithm has many applications in combinatorial optimization, for example in Traveling Salesman problem.



This package includes a Simulink model of the evaportaor described by Newell and Lee (1989) together with a gradient control system which achieves self-optimizing operation in terms of minimizing the operation cost. The work has been published in...



Complex step differentiation (CSD) is a superior numerical differentiation approach. Normally, it is more efficient and accurate than finite difference appraoch. This package provides a way to improve the MATLAB fmincon function using the CSD.



Complex step differentiation (CSD) has many advantages in efferency and accuracy over finite difference approaches (central, forward and backward). This code demonstrates how the Jacobian matrix of a given function at the reference point can be...



This is a small but efficient tool to perform K-nearest neighbor search, which has wide Science and Engineering applications, such as pattern recognition, data mining and signal processing.

The code was initially implemented through...



Identifying the Pareto Front from a set of points in a multi-objective space is the most important and also the most time-consuming task in multi-objective optimization. Usually, this is done through so called nondominated sorting. In this...



The zip file includes the model description in a pdf file, a simulink model of the steam condenser, a function to perform Reaction Curve PID tuning and an m-file to run the model.

The m-file can be used to learn how to use the Reaction...



This function implements bivariant Gaussian kernel density estimation. It can be used to estimate bivariant probability density function (pdf), cumulative distribution function (cdf) and inversed cdf (icdf) from a set of random data. The code is...



Based on the Gaussian kernel density estimation, it is possible to update the PDF estimation upon receiving new data by using the same bandwidth.

Without providing any previous estimation, this function does normal Gaussian kernel...