# maths Published articles for maths. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## MiniDXNN v0.4.0: Interactive neural texture compression on DirectX 12 DevFeed: [MiniDXNN v0.4.0: Interactive neural texture compression on DirectX 12]() Original publisher: [Read original article]() Author: Takahiro Harada; Sho Ikeda Published: 2026-08-13T14:30:00Z Content type: release Language: en Sources: [AMD GPUOpen]() Topics: [Compression](), [GPU](), [Inference](), [mlp](), [Machine learning](), [GUI](), [shaders](), [Open Source]() Tags: [agility-sdk](), [announcement](), [compression](), [directx](), [driver](), [getting-started](), [gpu](), [gpu-open-sdks](), [gpu-open-tools](), [gpuopen-sdks](), [gpuopen-tools](), [graphics-apis](), [gui](), [inference](), [maths](), [memory](), [microsoft](), [microsoft-agility-sdk](), [microsoft-directx](), [ml](), [mlp](), [neural](), [open-source](), [product-release](), [quick-start](), [real-time](), [shaders](), [technical-article](), [technical-articles](), [tools]() ### AI overview MiniDXNN v0.4.0 is an open-source library for GPU-accelerated MLP inference and training on DirectX 12. The release adds D3D12 Linear Algebra support, input encoding for neural texture compression, and a real-time GUI application for training and visualizing texture representations. ### Source excerpt MiniDXNN v0.4.0 introduces D3D12 Linear Algebra (SM 6.10) support, input encodings and neural texture compression, plus a real-time GUI app that trains and visualizes GPU-accelerated MLPs on DirectX® 12. ## GPU view-adaptive crack-free subdivision of Bézier surfaces DevFeed: [GPU view-adaptive crack-free subdivision of Bézier surfaces]() Original publisher: [Read original article]() Author: Bastian Kuth; Quirin Meyer Published: 2026-08-06T12:00:00Z Content type: tutorial Language: en Sources: [AMD GPUOpen]() Topics: [GPU](), [procedural geometry](), [Hardware]() Tags: [adaptive](), [article-release](), [gpu](), [graphics](), [graphics-apis](), [maths](), [memory](), [microsoft-work-graphs](), [optimization](), [performance](), [render](), [research](), [technical-article](), [technical-articles](), [techniques](), [white-paper](), [work-graphs]() ### AI overview This article explains a GPU work graph approach to recursively subdividing bicubic Bézier surfaces. The method adapts triangle density to curvature and camera distance, reducing unnecessary geometry while maintaining crack-free rendering. ### Source excerpt Learn how fast, crack-free GPU work graph subdivision for bicubic Bézier surfaces dramatically reduce triangle counts while simplifying implementation and matching hardware-tessellation quality. ## WMMA guide for AMD RDNA 4 architecture GPUs - part 3 DevFeed: [WMMA guide for AMD RDNA 4 architecture GPUs - part 3]() Original publisher: [Read original article]() Author: Hui Zhang Published: 2026-06-02T19:02:00Z Content type: tutorial Language: en Sources: [AMD GPUOpen]() Topics: [Code](), [Optimization](), [llama.cpp](), [Warp]() Tags: [3](), [amd](), [architecture](), [code](), [cpp](), [cuda](), [getting-started](), [guide](), [hip](), [implement](), [llama-cpp](), [maths](), [memory](), [ml](), [optimization](), [performance](), [rdna](), [reference-guide](), [technical-article](), [technical-articles](), [user-guides-manuals]() ### AI overview This tutorial explains how to implement an in-register matrix transpose using the WMMA layout on AMD RDNA 4 GPUs. It describes an identity-matrix approach that avoids additional memory operations and notes its use in Llama.cpp for Flash Attention. ### Source excerpt Learn how to implement fast in-register matrix transpose on AMD RDNA™ 4 architecture GPUs with a WMMA-based identity trick, delivering a lightweight, memory-free alternative proven in Llama.cpp. ## Fast calculation of the distance to cubic Bezier curves on the GPU DevFeed: [Fast calculation of the distance to cubic Bezier curves on the GPU]() Original publisher: [Read original article]() Published: 2025-10-18T09:21:56Z Content type: article Language: en Sources: [The Last Static Blog RSS]() Topics: [GPU](), [glsl](), [WebGL](), [shaders](), [pixel](), [Code]() Tags: [glsl](), [gpu](), [graphics](), [math](), [maths](), [pixel](), [prog](), [shaders](), [source](), [visualization](), [webgl](), [webgl-demo]() ### AI overview This article explains how to calculate the distance from a point to a cubic Bézier curve on the GPU. It derives the distance function and its derivative, reducing the problem to solving a fifth-degree polynomial, and demonstrates the method with self-contained GLSL fragment shaders and WebGL visualizations. ### Source excerpt Bézier curves are a core building block of text and 2D shapes rendering. There are several approaches to rendering them, but one especially challenging problem, both mathematically and technically, is computing the distance to a Bézier curve. For quadratic curves (one control point), this is fairly accessible, but for cubic (two control points) we're going to see why it is so hard. A glyph from the Virgil font, composed of multiple Bézier curves Having this distance field opens up many rendering possibilities. It's hard, but it's possible; here is a live proof: Distance to a cubic Bézier curve In this visualization, I'm borrowing your device resources to compute the distance to the curve for every single pixel. The yellow points are the control points of the curve (in white) and the blue zone is a representation of the distance field. Note All the demos and code in this article are self-contained GLSL fragment shaders. Most of the code can be found in the article, but feel free to inspect the source code of any of these WebGL demo for the complete code. They can be run verbatim using ShaderWorkshop. The basic maths In a previous article, we explained that a Bézier curve can be expressed as a polynomial. In our case, a cubic polynomial: B_3(t) = \textbf{a}t^3 + \textbf{b}t^2 + \textbf{c}t + \textbf{d} Where a, b, c and d are the vector coefficients derived from the start (P_0), end (P_3), and control points (P_1, P_2) using the following formulas (you can refer to the previous article for details): \begin{aligned} \textbf{a} &= -P_0 + 3(P_1-P_2) + P_3 \\ \textbf{b} &= 3P_0 - 6P_1 + 3P_2 \\ \textbf{c} &= -3P_0 + 3P_1 \\ \textbf{d} &= P_0 \end{aligned} For a given point p in 2D space, the distance to that Bézier curve can be expressed as a length between our curve and p: \begin{aligned} d(t) &= ||B_3(t) - \textbf{p}|| \\ &= ||\textbf{a}t^3 + \textbf{b}t^2 + \textbf{c}t + \textbf{d} - \textbf{p}|| \end{aligned} Our goal is to find the t value where d(t) is the smallest. ## How NuminaMath Won the 1st AIMO Progress Prize DevFeed: [How NuminaMath Won the 1st AIMO Progress Prize]() Original publisher: [Read original article]() Author: Yann Fleureau; LI Jia; Edward Beeching; Lewis Tunstall; Ben Lipkin; Roman Soletskyi; Shengyi Costa Huang; Kashif Rasul Published: 2024-07-11T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Mathematics](), [Artificial Intelligence](), [Open Source](), [AI Models](), [Fine-tuning](), [hugging face]() Tags: [ai-models](), [ai4math](), [artificial-intelligence](), [blog](), [blog-post](), [community](), [fine-tuning](), [hugging-face](), [leaderboard](), [math](), [maths](), [nlp](), [number-theory](), [open-science-collab](), [open-source](), [research](), [science]() ### AI overview This article introduces the Numina initiative and describes the technical work behind its winning solution for the 2024 AIMO progress prize. It focuses on open development of AI models for mathematical reasoning, including the role of LLM fine-tuning and support from Hugging Face and other organizations. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science.