Conventional quantum algorithms are not feasible for solving combinatorial optimization problems (COPs) with constraints in the operation time of quantum computers. To address this issue, researchers ...

Recently, a new approach for optimization of conditional value-at-risk (CVAR) was suggested and tested with several applications. For continuous distributions, CVAR is defined as the expected loss ...

A framework based on advanced AI techniques can solve complex, computationally intensive problems faster and in a more more scalable way than state-of-the-art methods, according to a new study. A ...

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 ...

AI founders are racing to secure GPUs — but the real constraint may be electricity.