Classes for Function Root-Finding

In the BzzMath library, the following classes are implemented for function rootfinding  

The BzzFunctionRoot class is designed to search for the root of a function when an interval of uncertainty is given and when the user wants to find one solution vfithin that interval. 

The function must be continuous within the interval. The BzzFunctionRoot class will not benefit from several processors. 

Conversely, if multiprocessor computers and openMP directives are used, the BzzFunctionRootMP class should be adopted under the same above-mentioned condition. It is worthwhile remarking that if we use objects from the BzzFunctionRootMP class, we must also use a compiler that supports openMP directives and activate them. 

The BzzFunctionRootRobust class is designed to search for root functions in the foliovfing cases  

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The inverse rational interpolation method is used as the basic method when more than three iterations of the BzzMath classes for function root-finding have been performed. 

If the function to be zeroed has a single root and changes sign during the integration of the system, an object from the BzzFunctionRoot class can be used. The root-finding classes can be combined with classes for differential systems based on multivalue algorithms (see Vol. 4 - Buzzi-Ferraris and Manenti, in press). 

The Decision tree method is widely used for classification and regression. A decision tree is a flow-chart-like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and leaf nodes represent classes or class distributions. In order to classify an unknown sample, the attribute values of the sample are tested according to the decision tree starting from the root until one of the leaves. To build decision trees, a data mining algorithm recursively inspects the available data set to find decisions that optimally split the data into distinguished subsets. An important property of this technique is that its functioning is easily understood. 

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