FPMCO decomposes multi-constraint RL into KL-projection sub-problems, achieving higher reward with lower computing than second-order rivals on the new SCIG robotics benchmark.

DeepSeek has introduced Manifold-Constrained Hyper-Connections (mHC), a novel architecture that stabilizes AI training and ...

UAV swarms have shown immense potential for applications ranging from disaster response to military reconnaissance, but ensuring reliable communication in contested environments has remained a ...

Right now you can pass constraints to your optimization problem, if the selected algorithm supports it. It would be interesting to be able to select an algorithm that supports unconstrained ...

Abstract: Expensive constrained optimization problems are prevalent in many engineering domains, where evaluating objective and constraints requires costly simulations or physical experiments. As ...

Implementation of numerical optimization algorithms in MATLAB, including derivative-free and gradient-based methods for unconstrained problems, and projection techniques for constrained optimization.

This study introduced an efficient method for solving non-linear equations. Our approach enhances the traditional spectral conjugate gradient parameter, resulting in significant improvements in the ...

Abstract: The utilization of both constrained and unconstrained-based optimization for solving constrained multi-objective optimization problems (CMOPs) has become prevalent among recently proposed ...

About 65% of organizations now use generative AI, or almost double the number seen in a similar study last year, a McKinsey survey found. This shift suggests that to remain visible, businesses and ...

Constrained quantization for a Borel probability measure refers to the idea of estimating a given probability by a discrete probability with a finite number of supporting points lying on a specific ...