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 ...

We consider the specialization of the primal simplex algorithm to the problem of finding a tree of directed shortest paths from a given node to all other nodes in a network of n nodes or finding a ...

Constrained network models describe a wide variety of real-world applications ranging from production, inventory, and distribution problems to financial applications. These problems can be solved with ...

PROC NETFLOW uses the Primal Simplex Network algorithm and the Primal Partitioning Algorithm to solve constrained network problems. These algorithms are fast, since they take advantage of algebraic ...

Abstract: Efficient optimization strategies for traffic engineering and routing are important tools for dimensioning networks and provisioning bandwidth for different applications. While current ...