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Showing 1 to 3 of 3 for “"Non-differentiable optimization"”.
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New model-based methods for non-differentiable optimization
Model-based optimization methods are effective for solving optimization problems with little structure, such as convexity and differentiability. Such algorithms iteratively find candidate solutions by generating samples from a parameterized probabilistic model on the solution space, and update the …
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Design Optimization of Fuzzy Logic Systems
… it is often necessary to undertake a design optimization process in which the adjustable parameters defining a particular fuzzy system are tuned to maximize a given performance criterion. Some data to approximate are commonly available and yield what is called the supervised learning problem. …
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Enhanced Formulations for Minimax and Discrete Optimization Problems with Applications to Scheduling and Routing
… formulations associated with such minimax optimization problems. Next, we explore novel continuous nonconvex as well as lifted discrete formulations for the notoriously challenging class of job-shop scheduling problems with the objective of minimizing the maximum completion time (i.e., …