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 ...
In mid-May, OpenAI announced that an internal AI model had disproved the Erdős unit distance conjecture, a famous problem in discrete geometry that had stumped human mathematicians for the last 80 ...
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 ...
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 ...
Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...
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 ...
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 ...
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