Features

• Linear optimization: revised simplex with sensitivity analysis, primal affine scaling, primal-dual interior-point method, and branch-and-bound methods
• Quadratic programming: affine scaling method with Karush-Kuhn-Tucker improvement
• Shortest-path tasks: one-to-all and all-to-all shortest connections on a graph, including a new and very efficient point-to-point routing algorithm and an improved Dijkstra's algorithm
• Combinatorial optimization and heuristics: several routines that can be used to solve assignment, traveling salesman, and other problems and can be used for implementation of additional effective heuristics, notably tabu search
• Reinforcement learning: a relatively new method for stochastic optimization and control that is quite powerful for some types of problems
• Financial risk analysis and portfolio management
• Documentation fully compatible with Mathematica 6