machine-learning optimization constrained-optimization hyperparameter-optimization meta-heuristic simulated-annealing hill-climbing bayesian-optimization nelder-mead random-search ...

Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.

This paper designs a distributed algorithm for large-scale constrained optimization problems, where the agents cooperate with each other based on local information and communication information. The ...

Distributed Constraint Optimisation and Search Algorithms form a vital framework for addressing complex decision‐making and scheduling problems in multi-agent systems.

Nonconvex optimization problems with an L1-constraint are ubiquitous, and are found in many application domains including: optimal control of hybrid systems, machine learning and statistics, and ...

Whether it’s choosing what to eat or what to read, recommendation systems are what make digital products human. They close ...

Human–Machine Collaboration: Industry 5.0 values the unique qualities of human workers, intuition, empathy, and ethical judgment, augmenting rather than replacing them through collaborative robots ...

Fusulines thrived in cold seas but vanished twice when warming from volcanoes rapidly spiked ocean carbon levels.

TAMP is a GPU-accelerated TAMP framework that solves complex robotic planning tasks in seconds by evaluating thousands of ...

Record pricing and pre-booked facilities will force companies to rethink infrastructure strategies amid an AI-driven market ...