Traditional network optimization methods such as dynamic and mixed-integer programming work well for small network optimizations. Large modern networks with complex protection and stability requirements need more.
MATE uses highly efficient approximation methods to control the explosion of complexity and lack of predictability normally associated with the solution of large-scale network optimization problems. These methods include probabilistic approximations, convex relaxations, and heuristic methods developed through experimentation on a range of planned and currently operational networks.
MATE uses statistical prediction methods to model future network demand and usage. Robust convex optimizations incorporate both these predictions and their estimated uncertainty in their design and engineering recommendations.
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