— Since Witsenhausen put forward his remarkable counterexample in 1968, there have been many attempts to develop efficient methods for solving this non-convex functional optimiz...
Selection of the topology of a neural network and correct parameters for the learning algorithm is a tedious task for designing an optimal artificial neural...
Recently proposed l1-regularized maximum-likelihood optimization methods for learning sparse Markov networks result into convex problems that can be solved optimally and efficien...
Existing index selection tools rely on heuristics to efficiently search within the large space of alternative solutions and to minimize the overhead of using the query optimizer ...
Developing dispatching rules for manufacturing systems is a tedious process, which is time- and cost-consuming. Since there is no good general rule for different scenarios and ob...