Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with pseudo-inverse training implemented using JavaScript. Compared to other training techniques, such as ...
Over the course of the early 2020s, businesses have faced a barrage of challenges that few could’ve predicted. From global supply chain disruptions and pandemic-driven shutdowns to on-and-off tariffs, ...
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 ...
Enterprises across industries, from energy to finance, use optimization models to plan complex operations like supply chains and logistics. These models work by defining three elements: the choices ...
Abstract: Optical neural networks (ONNs) have attracted great attention due to their low energy consumption and high-speed processing. The usual neural network training scheme leads to poor ...
This project implements a CVaR-minimizing portfolio optimization model based on the seminal paper "Optimization of Conditional Value-at-Risk" by Rockafellar and Uryasev (2000). The analysis uses ...
Abstract: The ever-increasing automation of complex decisionmaking processes through Operations Research (OR) raises the need for specialized management systems. To support multiple vertical ...
LinSpire maintains bounds for variables subject to linear (in)equalities and supports fast, incremental updates. It can retract constraints in any order and produces concise conflict explanations when ...
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