Tensor networks enable researchers to tackle quantum physics problems previously thought to be solvable only by quantum computers. Credit: Lucy Reading-Ikkanda/Simons Foundation By applying a 1980s ...

The new framework from the General Services Administration pulls together internal lessons on process improvement and automation, with officials now looking to scale adoption across government through ...

Abstract: The Tardy/Lost (TL) penalties scheduling is a discrete optimization problem. TL scheduling problem is an NP-hard problem. As a result, proposing an optimization algorithm to handle this ...

Abstract: A simulated annealing (SA) algorithm is an effective method for solving optimization problems, especially for combinatorial optimization problems. However, SA algorithms rely heavily on the ...

mlrose is a Python package for applying some of the most common randomized optimization and search algorithms to a range of different optimization problems, over both discrete- and continuous-valued ...

This is the official implementation of our paper "Riemannian Optimization on Relaxed Indicator Matrix Manifold" . We propose a fundamental manifold in machine learning—the Relaxed Indicator Matrix ...

We review encoding and hardware-independent formulations of optimization problems for quantum computing. Using this generalized approach, an extensive library of optimization problems from the ...

Multiple objectives to be optimized simultaneously are prevalent in real-life problems. This paper develops a new Pareto Method for bi-objective optimization which yields analytical solutions. The ...

Everywhere, AI is breaking. And everywhere, it’s breaking us. The breaking ensues whenever AI encounters ambiguity or volatility. And in our hazy, unstable world, that’s all the time: Either the data ...