We study first-order optimality for constrained composite optimization problems whose objective is the sum of a locally Lipschitz function and a proper lower semicontinuous function. We focus on the case in which the limiting subdifferential of the latter function is empty at a point of interest, so that the usual KKT conditions are unavailable. We … Read more
Many optimization problems solved repeatedly in practice, such as in power systems or logistics, share a fixed structure but with different parameter values. Re-optimizing each instance from scratch can be computationally prohibitive, while adopting a single solution for all realizations is overly conservative. The K-adaptability paradigm offers a flexible compromise by precomputing a small set … Read more
We study the problem of learning revenue-maximizing bundle prices when customer valuations and market composition are unknown and the firm observes only customer choices. This problem is challenging because the number of bundle-price decisions grows exponentially with the number of items offered. We develop BLMP (Bundle Pricing via Learning the Market from Customer Preferences), a … Read more