Simplex optimization is one of the simplest algorithms available to train a neural network. Understanding how simplex optimization works, and how it compares to the more commonly used back-propagation ...

New Features: PROC NETFLOW PROC NETFLOW solves network problems that can have side constraints. Certain algebraic features of networks are exploited by a specialized version of the Simplex method so ...

Termination Criteria for NLPNMS Since the Nelder-Mead simplex algorithm does not use derivatives, no termination criteria are available that are based on the gradient of the objective function. When ...