Research conducted by the Microsoft Research lab in Cambridge could contribute to the development of AI accelerators.

Despite the widespread use of established optimization algorithms like Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), Non-Dominated Sorting Genetic Algorithm-III (NSGA-III), and Multi-Objective ...

A Microsoft analog optical computer has solved two optimization problems and shown potential for AI workloads using less energy.

In an era of intensified global competition and increased demand fluctuations, traditional supply chain management is facing unprecedented challenges. The rise of AI technology has brought about a ...

In an era where autonomous systems demand pinpoint accuracy, navigation algorithms face a tough trade-off between precision and speed.

This paper addresses the shortcomings of the Sparrow and Eagle Optimization Algorithm (SBOA) in terms of convergence accuracy, convergence speed, and susceptibility to local optima. To this end, an ...

The class of optimization algorithms in machine learning is capable of tuning model parameters to minimize arguments of loss functions, for better prediction accuracy. Familiarity with these ...

Almost-Linear-Time Algorithms for Maximum Flow and Minimum-Cost Flow We present an algorithm that computes exact maximum flows and minimum-cost flows on directed graphs with m edges and polynomially ...

Section 3 provides a detailed description of the implementation steps of the adaptive grid multi-objective particle swarm optimization algorithm based on the SGP surrogate (AG-MOPSO-GPS) model.

This note investigates a network optimization problem in which a group of agents cooperate to minimize a global function under the practical constraint of finite-bandwidth communication. We propose an ...