When we talk about the cost of AI infrastructure, the focus is usually on Nvidia and GPUs — but memory is an increasingly important part of the picture. As hyperscalers prepare to build out billions ...

Low-rank data analysis has emerged as a powerful paradigm across applied mathematics, statistics, and data science. With the rapid growth of modern datasets in size, dimensionality, and complexity, ...

ABSTRACT: Multi-objective optimization remains a significant and realistic problem in engineering. A trade-off among conflicting objectives subject to equality and inequality constraints is known as ...

Abstract: This paper proposes a productivity optimization model based on mathematical modeling, combined with an improved optimization algorithm, aiming to improve the computational efficiency and ...

Abstract: Data is characterized by large scale, diversity, velocity and low value density in the Internet of Everything era. Traditional relational databases face significant challenges in efficiently ...

High-performance limit order book engine with C++ core and Python SDK. Processes 20M+ msgs/sec with µs latency. Supports real crypto/equity data replay, spread/imbalance/impact analytics, and ...

Reinforcement learning (RL) plays a crucial role in scaling language models, enabling them to solve complex tasks such as competition-level mathematics and programming through deeper reasoning.

Experiments demonstrating how theoretical space-time tradeoffs manifest in practice with 100-10,000× slowdowns due to memory hierarchies. Includes maze solving, checkpointed sorting, stream processing ...