Combinatorial optimization problems are often encountered in real-world applications, including logistics, scheduling and ...

The next wave of robotics depends on unifying code and hardware—embedding AI directly into the deterministic systems that ...

Abstract: This paper proposes an alternating refined constraint method (ARCM) for solving multi-objective optimization problems (MOPs) in energy systems. Through a two-stage solution mechanism, ARCM ...

Abstract: Constrained optimization is popularly seen in reinforcement learning (RL) for addressing complex control tasks. From the perspective of dynamic system, iteratively solving a constrained ...

Selling to other businesses has never been simple, but in 2026 the game has shifted significantly. Buyers self-educate before they ever speak to a sales rep. Committees of six to thirteen people weigh ...

The evolution of safety-critical autonomous systems, including agile drones and surgical robots, has fundamentally increased the demands for control design. It is no longer sufficient for a controller ...

Solving complex optimization problems is central to many modern technologies, from logistics and financial modeling to chip ...

Clarkson University researchers have developed an artificial intelligence tool that can uncover the mathematical equations ...

Prediction markets have moved from a niche curiosity to a legitimate asset class almost overnight, with annualized revenue already above $3B and analysts projecting a path toward $10B by 2030. Yet the ...

Formulation complexity, especially in small molecule drugs, has been getting more attention as capabilities continue to ...

In recent years, the frequency of weather-related natural disasters—cyclones, torrential rains, floods—has increased as a consequence of global warming. These disasters cause billions of dollars in ...