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Everything about Matrix multiplication algorithm

Algorithm to multiply matrices

Because matrix multiplication is such a central operation in many numerical algorithms, much work has been invested in making matrix multiplication algorithms efficient. Applications of matrix multiplication in computational problems are found in many fields including scientific computing and pattern recognition and in seemingly unrelated problems such as counting the paths through a graph. Many different algorithms have been designed for multiplying matrices on different types of hardware, including parallel and distributed systems, where the computational work is spread over multiple processors. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Matrix multiplication algorithmA 5-minute orientation
  2. Skim a recent paper: Discovering faster matrix multiplication algorithms with reinforcement learningInfluential work since 2022
  3. Pick up a classic book: Neural Information ProcessingThe most-reprinted book on the subject
  4. Read the landmark paper: The Mailman algorithm: A note on matrix–vector multiplicationCited 59 times
  5. Go sideways: Strassen algorithmA closely related topic
  6. Go sideways: Computational complexity of matrix multiplicationA closely related topic

📖 Definitions

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🗂️ The subject at a glance

  1. Iterative algorithm
  2. Divide-and-conquer algorithm
  3. Sub-cubic algorithms
  4. Parallel and distributed algorithms

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

🔗 Related topics