US quantum computing company IonQ and the Oak Ridge National Laboratory report having demonstrated how the power of quantum can support grid operators to meet emerging challenges. The demonstration ...

Abstract: We put forth a hybrid-computing solution to a class of constrained nonlinear optimization problems involving nonlinear cost and linear constraints. This is accomplished by realizing gradient ...

A Benchmark of Kriging-Based Infill Criteria for Noisy Optimization (Victor Picheny et al., Structural and Multidisciplinary Optimization 2013) 📖 Scalable Test Problems for Evolutionary ...

ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...

Abstract: Portfolio optimization that allows the borrowed money from a loan to be invested in risk assets is formulated as a constrained optimization problem. Then it is proved that the optimal ...

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 ...

A bead optimization problem to minimize the strain energy of a square plate - supported at the corners and subject to a single force in the center. In the table below, a comparison with manual ...

1 School of Mathematics and Statistics, Fuzhou University, Fuzhou, China. 2 College of Computer and Data Science, Fuzhou University, Fuzhou, China. In this paper, we use Physics-Informed Neural ...

Resource allocation of wide-area internet networks is inherently a combinatorial optimization problem that if solved quickly, could provide near real-time adaptive control of internet-protocol traffic ...

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 study led by ...