gonum/gonum

Gonum is a set of numeric libraries for the Go programming language. It contains libraries for matrices, statistics, optimization, and more

Gonum is a collection of packages written in Go. The core code is pure Go with some assembly for performance.

  • What it does: Provides building blocks for numerical and graph work in Go — the README mentions graph-related packages (topo, graph/formats/dot, graph/formats/rdf) but does not list the full package set.
  • Installation: go get -u gonum.org/v1/gonum/...
  • Supported Go versions: The two most recent Go releases, using the gc compiler, on Linux (386, amd64, arm64), macOS, and Windows (amd64). Floating-point behavior may differ between compilers and architectures.
  • Release schedule: A new version every six months, aligned with Go releases — ~February and ~August.
  • Build options: Several flags change compilation:
    • safe — avoid assembly and unsafe code
    • bounds — add bounds checks even in internal calls
    • noasm — skip assembly implementations
    • tomita — use a specific pivot choice for maximal clique calculation (otherwise a random pivot is used; applies to the topo graph package)
  • Getting help / reporting bugs: GitHub issue tracker for bugs; the gonum-dev Google Group for API changes, feature requests, and code review discussions.
  • Licensing: Original code is under the Gonum License. Some parts have additional licenses (in THIRD_PARTY_LICENSES, all BSD or MIT). The graph/formats/dot code is dual-licensed (Public Domain Dedication or Gonum License). The W3C test suites in graph/formats/rdf use the W3C Test Suite License and the W3C 3-clause BSD License.

Related

  • Project
    HenryNdubuaku/maths-cs-ai-compendium

    An open, intuition-first textbook covering maths, computing, and AI across 18 chapters, born from the author's personal notes that helped friends land roles at DeepMind, OpenAI, and Nvidia. Includes an MCP server for AI assistants, a learning philosophy aimed at "the Darwins of the world," and a two-phase study technique.

  • Project
    KalyanM45/AI-Project-Gallery

    A catalog that lists 33 AI/ML projects by domain, links each to its GitHub repo, marks 14 as end-to-end, and previews 10 more projects as "Coming Soon". The README is a simple, table-based index with no explanation of project internals.

  • Project
    aimacode/aima-python

    Python implementations of the algorithms from the book 'Artificial Intelligence: A Modern Approach', providing a practical reference for studying classic AI concepts.

  • Project
    LAMDA-NeSy/Research-Starter-Kit

    Research-Starter-Kit is an open-source Chinese handbook by a Nanjing University professor that teaches research beginners how to do research and write papers, covering everything from finding papers to writing each section and handling rebuttals. It's early-stage, free, and aimed at students entering labs for the first time.