Randall Morck of the Alberta School of Business says economists too often regard people as "dynamic, stochastic optimization ...

Learn how to implement SGD with momentum from scratch in Python—boost your optimization skills for deep learning.

Abstract: The increasing radio frequency interference (RFI) caused by other in-band radio frequency users, such as communication systems, affects the waveform-agile tracking performance of cognitive ...

Abstract: In this article, we deal with stochastic optimization problems where the data distributions change in response to the decision variables. Traditionally, the study of optimization problems ...

Mini Batch Gradient Descent is an algorithm that helps to speed up learning while dealing with a large dataset. Instead of ...

Alphabet delivers an integrated AI stack with TPUs, data scale, and near-zero inference costs, plus targets and key risks.

SuperQ Quantum Computing Inc. (CSE: QBTQ) (OTCQB: QBTQF) (FSE: 25X) ("SuperQ Quantum", "SuperQ", or the "Company"), a leader ...

Traffic congestion, fuel consumption, and emissions also offer quantifiable performance indicators, making mobility uniquely ...

Nanoscale device employs magnetic tunnel junctions to convert thermal noise into binary signals for random number generation.

Aegis Critical Energy Defence Corp. (CSE: QESS) (OTCQB: QESSF) (FSE: JG6) ("Aegis"), a leading developer and integrator of advanced energy storage systems for defence, critical infrastructure, ...

Fruchter, G. (2026) Opportunism in Supply Chain Recommendations: A Dynamic Optimization Approach. Modern Economy, 17, 26-38.

SuperQ Quantum Computing Inc. (CSE: QBTQ) (OTCQB: QBTQF) (FSE: 25X) ("SuperQ Quantum", "SuperQ", or the "Company"), a leader in hybrid quantum-classical ...