We use heuristics all the time across many systems including those that are critical to production services. Production systems use heuristics because they are faster or scale better than their ...

Abstract: This paper introduces a hybrid optimisation framework that integrates Genetic Algorithms (GAs) and Reinforcement Learning (RL) for the construction of high-order Runge–Kutta (RK) schemes.

We combine Mixed-Integer Programming (MIP) with Machine Learning to find near-optimal portfolios efficiently: maximize: μᵀw - λ·(wᵀΣw) - transaction_costs(w ...

ABSTRACT: Mathematical optimization is a fundamental aspect of machine learning (ML). An ML task can be conceptualized as optimizing a specific objective using the training dataset to discern patterns ...

A lightweight, in-memory analytical database engine that processes CSV files using relational operators and query optimization techniques. This system implements core database concepts including the ...

It’s the most transparent estimate yet from one of the big AI companies, and a long-awaited peek behind the curtain for researchers. Google has just released a technical report detailing how much ...

Learning a new skill can be difficult, especially when it’s relatively complex. It’s often hard to keep definitions, concepts, and descriptions straight when you’re trying to make inroads into an area ...

The binary paint shop problem (BPSP) is an APX-hard optimization problem of the automotive industry. In this work, we show how to use the quantum approximate optimization algorithm (QAOA) to find ...