We propose a programming paradigm called compress-and-conquer (CC) that leads to optimal performance on multicore platforms. Given a multicore system of p cores and a problem of s...
We apply the method known as simulated annealing to the following problem in convex optimization: minimize a linear function over an arbitrary convex set, where the convex set is ...
Automated feature discovery is a fundamental problem in machine learning. Although classical feature discovery methods do not guarantee optimal solutions in general, it has been r...
The Steel Mill Slab Problem is an optimization benchmark that has been studied for a long time in the constraint-programming community but was only solved efficiently in the two l...
Pierre Schaus, Pascal Van Hentenryck, Jean-Noë...
Constrained local models (CLMs) have recently demonstrated good performance in non-rigid object alignment/tracking in comparison to leading holistic approaches (e.g., AAMs). A maj...