Simons Institute for the Theory of Computing
@SimonsInstitute
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The world's leading venue for collaborative research in theoretical computer science. Follow us at https://t.co/KvcuGI7WM0.
Berkeley, CA
Joined January 2018
2/2 Multiplying 2 n x n matrices requires O(n^w) arithmetic operations, where w=3 for the brute force algorithm. Strassen's method was the first big improvement in '69 (w=2.81), followed by two big jumps in the '80s. The world record today is w=2.3714. https://t.co/x3V0Z9HXFR
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1/2 "It seems silly, but it's a very important problem." Virginia Vassilevska Williams (@MIT) on the progress in matrix multiplication algorithms during her Richard M. Karp Distinguished Lecture On Matrix Multiplication Algorithms at the Simons Institute. https://t.co/x3V0Z9HXFR
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Your ERP shouldn’t slow you down. See why top eCommerce brands like Ridge, HexClad, Grüns, and Kotn moved to Fulfil as their ERP.
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Ever wondered about graph learning? Watch Ameya Velingker (@ameya_pa) and Haggai Maron (@HaggaiMaron) give a masterful introduction at the Simons Institute's workshop on Graph Learning Meets Theoretical Computer Science. Video: https://t.co/R5cIal2R8L
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We’re delighted to be supported by the @GoogleDeepMind x @Googleorg AI for Math Initiative, which was launched today. The new generation of AI tools will transform research on the foundations of computing, and this new initiative is poised to accelerate that.
Together with @Googleorg, we’re introducing the AI for Math initiative, bringing together five prestigious research institutions pioneering the use of AI in mathematics. ⤵️
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I enjoyed this conversation with Yael and Daniele! Thanks @SimonsInstitute for the opportunity! #cryptography #security
In this episode of Polylogues, @science_eye sits down Yael Tauman Kalai and Daniele Micciancio to discuss developments in lattices and FHE, the gap between theory and practice, and the unique culture of crypto as a field. https://t.co/epmulOz587
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This week at the Simons Institute, a workshop on Matrix Multiplication. https://t.co/sn7y5IRl2J
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This week at the Simons Institute, a workshop on Matrix Multiplication. https://t.co/sn7y5IRl2J
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1/2 Should have paid attention to matrices during linear algebra classes! In 2026, AI will use ~1% of global electricity, of which ~45-90% will be for matrix multiplications, said Oded Schwartz of Hebrew University of Jerusalem at the Simons Institute. https://t.co/z3wBTWcJRL
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2/2 There are matrix multiplication algorithms that can do better than Strassen's but only for astronomically large matrices, making them impractical, said Oded Schwartz at the Simons Institute's workshop on Complexity and Linear Algebra Boot Camp. Video: https://t.co/z3wBTWcJRL
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1/2 Should have paid attention to matrices during linear algebra classes! In 2026, AI will use ~1% of global electricity, of which ~45-90% will be for matrix multiplications, said Oded Schwartz of Hebrew University of Jerusalem at the Simons Institute. https://t.co/z3wBTWcJRL
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Join us for Robert Tarjan's Richard M. Karp Distinguished Lecture on "Asynchronous Concurrency in Data Structures," Tuesday, October 21 at 3:30 p.m. Pacific Time. https://t.co/5GMLo0P5nF
simons.berkeley.edu
This talk will explore the question of whether and by how much operations on data structures can be sped up by using multiple unsynchronized processes. Taking advantage of concurrency in this setting...
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2/2 For multiplying two n x n matrices, the arithmetic complexity of the standard method is of O(n^3); Strassen's method is of O(n^2.81). Prof. Olga Holtz spoke at the Simons Institute's workshop on Complexity and Linear Algebra Boot Camp. Video: https://t.co/N3ep8k1q3U
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1/2 Matrix multiplications are central to machine learning. UC Berkeley Professor Olga Holtz's analyzed, from scratch, the arithmetic complexity of matrix multiplication using Strassen's fast algorithm. She spoke at the Simons Institute. Video: https://t.co/N3ep8k1q3U
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2/2 For multiplying two n x n matrices, the arithmetic complexity of the standard method is of O(n^3); Strassen's method is of O(n^2.81). Prof. Olga Holtz spoke at the Simons Institute's workshop on Complexity and Linear Algebra Boot Camp. Video: https://t.co/N3ep8k1q3U
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1/2 Matrix multiplications are central to machine learning. UC Berkeley Professor Olga Holtz's analyzed, from scratch, the arithmetic complexity of matrix multiplication using Strassen's fast algorithm. She spoke at the Simons Institute. Video: https://t.co/N3ep8k1q3U
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Simons Institute Senior Scientist Nikhil Srivastava on Diagonalization Algorithms, from the Complexity and Linear Algebra Boot Camp. https://t.co/4TNOBCgCTk
simons.berkeley.edu
In his presentation in the Complexity and Linear Algebra Boot Camp, Senior Scientist Nikhil Srivastava defines the problem of approximately diagonalizing a given dense matrix, and explains two...
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