In today’s retail world, too much inventory is as risky as carrying too little. One U.S. grocery chain, operating a hub-and-spoke distribution model, held 57 days of supply for dry food. Inventory ...
NVIDIA's GPU-accelerated cuOpt engine discovers new solutions for four MIPLIB benchmark problems, outperforming CPU solvers with 22% lower objective gaps. NVIDIA's cuOpt optimization engine has found ...
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 ...
Abstract: dynamic multiobjective optimization (DMO) problems are prevalent in many practical applications and have garnered significant attention from both industry and academia, leading to the ...
Abstract: Direct collocation (DC) is a widely used method for solving dynamic optimization problems (DOPs), but its implementation simplicity and computational efficiency are limited for challenging ...
We present a joint multi-robot trajectory optimizer that can compute trajectories for tens of robots in aerial swarms within a small fraction of a second. The computational efficiency of our approach ...
This work proposes a framework for global optimization, showing that global optimization is equivalent to optimal strategy formation in a two-armed decision problem with known distributions, based on ...
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