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COBYLA2 is an implementation of Powell's nonlinear derivative free constrained optimization that uses a linear approximation approach. The algorithm is a sequential trust region algorithm that employs linear approximations to the objective and constraint functions, where the approximations are formed by linear interpolation at n + 1 points in the space of the variables and tries to maintain a regular shaped simplex over iterations. It solves nonsmooth NLP with a moderate number of variables (about 100). Inequality constraints only. The initial point X is taken as one vertex of the initial simplex with zero being another, so, X should not be entered as the zero vector.

HotSpot learns a set of rules (displayed in a tree-like structure) that maximize/minimize a target variable/value of interest. With a nominal target, one might want to look for segments of the data where there is a high probability of a minority value occuring (given the constraint of a minimum support). For a numeric target, one might be interested in finding segments where this is higher on average than in the whole data set. For example, in a health insurance scenario, find which health insurance groups are at the highest risk (have the highest claim ratio), or, which groups have the highest average insurance payout.

The Systems Biology Simulation Core Library provides an efficient and exhaustive Java™ implementation of methods to interpret the content of models encoded in the Systems Biology Markup Language (SBML) and its numerical solution. This library is based on the JSBML project. It can be used on every operating system for which a Java Virtual Machine is available. Version 2.0 and beyond support simulation in three frameworks: constraint-based analysis, stochastic simulation, and ordinary differential equation systems. SBSCL supports SED-ML and COMBINE archives in OMEX format.

This is a plugin to Madura Objects. It provides a rules/constraint engine to assist with validation, deriving new values from user inputs (eg total of invoices entered on this customer) an manipulating metadata (eg because the amount is above X we make some field readonly). Note that the Java that is using the monitored objects is quite unaware of the rules layer implemented here. That means you can change rules without having to go back to your Java code, and it also means you don't have to wonder if everything implemented the same rules. Anything using that Java object has the rules (unless you turn them all off).