where \(\mathsf{G}(\cdot)\) is some convex operator and \(\mathcal{F}\) is as set of feasible input distributions. Examples of such an optimization problem include finding capacity in information ...

Statistical Science, Vol. 8, No. 1, Report from the Committee on Applied and Theoretical Statistics of the National Research Council on Probability and Algorithms (Feb., 1993), pp. 48-56 (9 pages) ...

This paper considers the problem of optimizing the ratio f Tr[VT AV]/Tr [VT BV] over all unitary matrices V with p columns, where A,B are two positive definite matrices. This problem is common in ...

The rise of AI, graphic processing, combinatorial optimization and other data-intensive applications has resulted in data-processing bottlenecks, as ever greater amounts of data must be shuttled back ...

Real-Time Optimization with Robustness and Acceleration via Hybrid Dynamical Systems and Averaging Theory In this talk we will discuss robust and accelerated zero-order algorithms for the solution of ...

A group of researchers at the Massachusetts Institute of Technology have devised a potentially more effective way of helping computers solve some of the toughest optimization problems they face. Their ...

In Part 1 of this series on optimization and recovery, we considered two limitations of optimization processes and the light they shed on pseudo-recovery. Let’s now think more about what the ...

What if the next new mathematical discovery didn’t come from a human mind, but from an AI? Imagine a machine not just crunching numbers but proposing original solutions to problems that have baffled ...