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Victoria Stuart 🇨🇦 🏳️‍⚧️These are clever people: Quantum Scientists Building New Math of Cryptography <a href="https://www.quantamagazine.org/quantum-scientists-have-built-a-new-math-of-cryptography-20250725/" rel="nofollow noopener" translate="no" target="_blank">https://www.quantamagazine.org/quantum-scientists-have-built-a-new-math-of-cryptography-20250725/</a> One-way function <a href="https://en.wikipedia.org/wiki/One-way_function" rel="nofollow noopener" translate="no" target="_blank">https://en.wikipedia.org/wiki/One-way_function</a> Quantum cryptography <a href="https://en.wikipedia.org/wiki/Quantum_cryptography" rel="nofollow noopener" translate="no" target="_blank">https://en.wikipedia.org/wiki/Quantum_cryptography</a> Permanent (mathematics) <a href="https://en.wikipedia.org/wiki/Permanent_(mathematics)" rel="nofollow noopener" translate="no" target="_blank">https://en.wikipedia.org/wiki/Permanent_(mathematics)</a> ♯P-completeness of 01-permanent <a href="https://en.wikipedia.org/wiki/%E2%99%AFP-completeness_of_01-permanent" rel="nofollow noopener" translate="no" target="_blank">https://en.wikipedia.org/wiki/%E2%99%AFP-completeness_of_01-permanent</a><a href="https://mastodon.social/tags/mathematics" class="mention hashtag" rel="nofollow noopener" target="_blank">#mathematics</a> <a href="https://mastodon.social/tags/cryptography" class="mention hashtag" rel="nofollow noopener" target="_blank">#cryptography</a> <a href="https://mastodon.social/tags/OneWayFunctions" class="mention hashtag" rel="nofollow noopener" target="_blank">#OneWayFunctions</a> <a href="https://mastodon.social/tags/LinearAlgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#LinearAlgebra</a> <a href="https://mastodon.social/tags/matrices" class="mention hashtag" rel="nofollow noopener" target="_blank">#matrices</a> <a href="https://mastodon.social/tags/QuantumCryptography" class="mention hashtag" rel="nofollow noopener" target="_blank">#QuantumCryptography</a> <a href="https://mastodon.social/tags/NPhard" class="mention hashtag" rel="nofollow noopener" target="_blank">#NPhard</a>

रञ्जित (Ranjit Mathew)“I Don’t Like NumPy”, ‘Dynomight’ (<a href="https://dynomight.net/numpy/" rel="nofollow noopener" translate="no" target="_blank">https://dynomight.net/numpy/</a>).Via HN: <a href="https://news.ycombinator.com/item?id=43996431" rel="nofollow noopener" translate="no" target="_blank">https://news.ycombinator.com/item?id=43996431</a><a href="https://mastodon.social/tags/NumPy" class="mention hashtag" rel="nofollow noopener" target="_blank">#NumPy</a> <a href="https://mastodon.social/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#Python</a> <a href="https://mastodon.social/tags/Math" class="mention hashtag" rel="nofollow noopener" target="_blank">#Math</a> <a href="https://mastodon.social/tags/LinearAlgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#LinearAlgebra</a> <a href="https://mastodon.social/tags/NumericalComputing" class="mention hashtag" rel="nofollow noopener" target="_blank">#NumericalComputing</a> <a href="https://mastodon.social/tags/Rants" class="mention hashtag" rel="nofollow noopener" target="_blank">#Rants</a>

Hacker NewsGraphical Linear Algebra<a href="https://graphicallinearalgebra.net/" rel="nofollow noopener" translate="no" target="_blank">https://graphicallinearalgebra.net/</a><a href="https://mastodon.social/tags/HackerNews" class="mention hashtag" rel="nofollow noopener" target="_blank">#HackerNews</a> <a href="https://mastodon.social/tags/Graphical" class="mention hashtag" rel="nofollow noopener" target="_blank">#Graphical</a> <a href="https://mastodon.social/tags/Linear" class="mention hashtag" rel="nofollow noopener" target="_blank">#Linear</a> <a href="https://mastodon.social/tags/Algebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#Algebra</a> <a href="https://mastodon.social/tags/LinearAlgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#LinearAlgebra</a> <a href="https://mastodon.social/tags/Graphics" class="mention hashtag" rel="nofollow noopener" target="_blank">#Graphics</a> <a href="https://mastodon.social/tags/DataVisualization" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataVisualization</a> <a href="https://mastodon.social/tags/MathEducation" class="mention hashtag" rel="nofollow noopener" target="_blank">#MathEducation</a>

katch wreck... that's nothing new. the point was to address a related question: suppose that the eigensystem {v_i, λ_i}, i = 1, ..., n of a full-rank, well-conditioned n-by-n square matrix A is known, and then you are given a related matrix B = A + E, where E represents some type of random noise. Can a relationship between E and c be derived, such that the eigensystem of A also satisfies f( B v_i - λ_i v_i ) <= c, for all i and some f?<a href="https://mastodon.social/tags/math" class="mention hashtag" rel="nofollow noopener" target="_blank">#math</a> <a href="https://mastodon.social/tags/mathematics" class="mention hashtag" rel="nofollow noopener" target="_blank">#mathematics</a> <a href="https://mastodon.social/tags/linearAlgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#linearAlgebra</a> <a href="https://mastodon.social/tags/eigenvalue" class="mention hashtag" rel="nofollow noopener" target="_blank">#eigenvalue</a> <a href="https://mastodon.social/tags/eigensystem" class="mention hashtag" rel="nofollow noopener" target="_blank">#eigensystem</a> <a href="https://mastodon.social/tags/algebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#algebra</a>

N-gated Hacker NewsIn this groundbreaking revelation, the author stretches the very fabric of reality by turning boring old functions into thrilling "infinite-dimensional vectors". 🧐 Because who doesn't want to apply linear algebra to every mundane aspect of life? 🤓🎉 Required reading: everything you've ever learned about math, ever. <a href="https://thenumb.at/Functions-are-Vectors/" rel="nofollow noopener" translate="no" target="_blank">https://thenumb.at/Functions-are-Vectors/</a> <a href="https://mastodon.social/tags/linearalgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#linearalgebra</a> <a href="https://mastodon.social/tags/mathrevolution" class="mention hashtag" rel="nofollow noopener" target="_blank">#mathrevolution</a> <a href="https://mastodon.social/tags/infinitedimensionalvectors" class="mention hashtag" rel="nofollow noopener" target="_blank">#infinitedimensionalvectors</a> <a href="https://mastodon.social/tags/thrillingmath" class="mention hashtag" rel="nofollow noopener" target="_blank">#thrillingmath</a> <a href="https://mastodon.social/tags/hackersnews" class="mention hashtag" rel="nofollow noopener" target="_blank">#hackersnews</a> <a href="https://mastodon.social/tags/HackerNews" class="mention hashtag" rel="nofollow noopener" target="_blank">#HackerNews</a> <a href="https://mastodon.social/tags/ngated" class="mention hashtag" rel="nofollow noopener" target="_blank">#ngated</a>

datatofuThis is called "A Gentle Introduction to the Hessian Matrix"Hessians are somewhere between <a href="https://mastodon.social/tags/linearalgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#linearalgebra</a> <a href="https://mastodon.social/tags/calculus" class="mention hashtag" rel="nofollow noopener" target="_blank">#calculus</a> and <a href="https://mastodon.social/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#rstats</a> but still a core aspect of <a href="https://mastodon.social/tags/datascience" class="mention hashtag" rel="nofollow noopener" target="_blank">#datascience</a> All in all, building and deriving things like these are probably only useful when developing a unique solution. For the vast majority of cases, having a general understanding is enough. ... actually, I am pretty sure that there is a <a href="https://mastodon.social/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#python</a> library for just such an occasion (I have never looked though so ymmv)

Alex NelsonHere's a question: let \(M\) be a \(0\times 0\) matrix with entries in the field \(\mathbb{F}\). What is \(\det(M)\)?<a href="https://mathstodon.xyz/tags/Mathematics" class="mention hashtag" rel="nofollow noopener" target="_blank">#Mathematics</a> <a href="https://mathstodon.xyz/tags/Determinant" class="mention hashtag" rel="nofollow noopener" target="_blank">#Determinant</a> <a href="https://mathstodon.xyz/tags/LinearAlgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#LinearAlgebra</a> <a href="https://mathstodon.xyz/tags/Matrix" class="mention hashtag" rel="nofollow noopener" target="_blank">#Matrix</a>

Giuseppe BilottaThat first implementation didn't even support the multi-GPU and multi-node features of <a href="https://fediscience.org/tags/GPUSPH" class="mention hashtag" rel="nofollow noopener" target="_blank">#GPUSPH</a> (could only run on a single GPU), but it paved the way for the full version, that took advantage of the whole infrastructure of GPUSPH in multiple ways.First of all, we didn't have to worry about how to encode the matrix and its sparseness, because we could compute the coefficients on the fly, and operate with the same neighbors list transversal logic that was used in the rest of the code; this allowed us to minimize memory use and increase code reuse.Secondly, we gained control on the accuracy of intermediate operations, allowing us to use compensating sums wherever needed.Thirdly, we could leverage the multi-GPU and multi-node capabilities already present in GPUSPH to distribute computations across all available devices.And last but not least, we actually found ways to improve the classic <a href="https://fediscience.org/tags/CG" class="mention hashtag" rel="nofollow noopener" target="_blank">#CG</a> and <a href="https://fediscience.org/tags/BiCGSTAB" class="mention hashtag" rel="nofollow noopener" target="_blank">#BiCGSTAB</a> linear solving algorithms to achieve excellent accuracy and convergence even without preconditioners, while making the algorithms themselves more parallel-friendly:<a href="https://doi.org/10.1016/j.jcp.2022.111413" rel="nofollow noopener" translate="no" target="_blank">https://doi.org/10.1016/j.jcp.2022.111413</a>4/n<a href="https://fediscience.org/tags/LinearAlgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#LinearAlgebra</a> <a href="https://fediscience.org/tags/NumericalAnalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#NumericalAnalysis</a>

Matthew AbbottI told myself to pick up a relaxing hobby for my evenings 🥲<a href="https://oliphaunt.social/tags/chinese" class="mention hashtag" rel="nofollow noopener" target="_blank">#chinese</a> <a href="https://oliphaunt.social/tags/math" class="mention hashtag" rel="nofollow noopener" target="_blank">#math</a> <a href="https://oliphaunt.social/tags/linearAlgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#linearAlgebra</a> <a href="https://oliphaunt.social/tags/learningIsFun" class="mention hashtag" rel="nofollow noopener" target="_blank">#learningIsFun</a> <a href="https://oliphaunt.social/tags/language" class="mention hashtag" rel="nofollow noopener" target="_blank">#language</a>

connorESSENCE OF LINEAR ALGEBRA by 3Blue1Brown I’ve discovered a potentially great Linear Algebra review series on YouTube by 3Blue1Brown. <a href="https://youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab" rel="nofollow noopener" target="_blank">https://youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab</a> I’ll be keeping review notes in this thread. <a href="http://social.connorbode.me/tags/study" class="mention hashtag" rel="nofollow noopener" target="_blank">#Study</a> <a href="http://social.connorbode.me/tags/math" class="mention hashtag" rel="nofollow noopener" target="_blank">#Math</a> <a href="http://social.connorbode.me/tags/linearalgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#LinearAlgebra</a>

FrederickThere is something so awe inspiring and satisfying about Linear Algebra when the math checks out and your experiments just *work*. The power behind matrix multiplication (among other linear operations) is incredible — it seemingly makes the impossible possible all while being relatively simple operations. Those neural networks are mostly matrix multiplications all the down and the power blows my mind even to this day.<a href="https://social.nerd.net/tags/machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#machinelearning</a> <a href="https://social.nerd.net/tags/linearalgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#linearalgebra</a> <a href="https://social.nerd.net/tags/math" class="mention hashtag" rel="nofollow noopener" target="_blank">#math</a> <a href="https://social.nerd.net/tags/maths" class="mention hashtag" rel="nofollow noopener" target="_blank">#maths</a>

Tammy Kolda👋 Ma(th)stodon friends! <a href="https://mathstodon.xyz/tags/Introduction" class="mention hashtag" rel="nofollow noopener" target="_blank">#Introduction</a> I’m an <a href="https://mathstodon.xyz/tags/AppliedMathematician" class="mention hashtag" rel="nofollow noopener" target="_blank">#AppliedMathematician</a> doing <a href="https://mathstodon.xyz/tags/consulting" class="mention hashtag" rel="nofollow noopener" target="_blank">#consulting</a>, <a href="https://mathstodon.xyz/tags/research" class="mention hashtag" rel="nofollow noopener" target="_blank">#research</a>, and writing on two forthcoming books.Research interests: <a href="https://mathstodon.xyz/tags/MathematicalDataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#MathematicalDataScience</a>, <a href="https://mathstodon.xyz/tags/TensorDecompositions" class="mention hashtag" rel="nofollow noopener" target="_blank">#TensorDecompositions</a>, <a href="https://mathstodon.xyz/tags/NumericalOptimization" class="mention hashtag" rel="nofollow noopener" target="_blank">#NumericalOptimization</a>, <a href="https://mathstodon.xyz/tags/LinearAlgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#LinearAlgebra</a>, <a href="https://mathstodon.xyz/tags/NetworkScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#NetworkScience</a>, <a href="https://mathstodon.xyz/tags/RandomizedAlgorithms" class="mention hashtag" rel="nofollow noopener" target="_blank">#RandomizedAlgorithms</a>, <a href="https://mathstodon.xyz/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#MachineLearning</a>, <a href="https://mathstodon.xyz/tags/HPC" class="mention hashtag" rel="nofollow noopener" target="_blank">#HPC</a>, …Champion for <a href="https://mathstodon.xyz/tags/diverity" class="mention hashtag" rel="nofollow noopener" target="_blank">#diverity</a>, <a href="https://mathstodon.xyz/tags/equity" class="mention hashtag" rel="nofollow noopener" target="_blank">#equity</a>, and <a href="https://mathstodon.xyz/tags/inclusion" class="mention hashtag" rel="nofollow noopener" target="_blank">#inclusion</a> (<a href="https://mathstodon.xyz/tags/dei" class="mention hashtag" rel="nofollow noopener" target="_blank">#dei</a>) in the mathematical sciences and <a href="https://mathstodon.xyz/tags/STEM" class="mention hashtag" rel="nofollow noopener" target="_blank">#STEM</a> at large.Other Interests: <a href="https://mathstodon.xyz/tags/AIFailures" class="mention hashtag" rel="nofollow noopener" target="_blank">#AIFailures</a> <a href="https://mathstodon.xyz/tags/LaTeX" class="mention hashtag" rel="nofollow noopener" target="_blank">#LaTeX</a> <a href="https://mathstodon.xyz/tags/TikZ" class="mention hashtag" rel="nofollow noopener" target="_blank">#TikZ</a> <a href="https://mathstodon.xyz/tags/PGFPlots" class="mention hashtag" rel="nofollow noopener" target="_blank">#PGFPlots</a> <a href="https://mathstodon.xyz/tags/Yoga" class="mention hashtag" rel="nofollow noopener" target="_blank">#Yoga</a> & (literary) <a href="https://mathstodon.xyz/tags/SciFi" class="mention hashtag" rel="nofollow noopener" target="_blank">#SciFi</a>

David BindelAn <a href="https://mathstodon.xyz/tags/intoduction" class="mention hashtag" rel="nofollow noopener" target="_blank">#intoduction</a> after moving instances: I'm a <a href="https://mathstodon.xyz/tags/prof" class="mention hashtag" rel="nofollow noopener" target="_blank">#prof</a> at <a href="https://mathstodon.xyz/tags/cornell" class="mention hashtag" rel="nofollow noopener" target="_blank">#cornell</a> in <a href="https://mathstodon.xyz/tags/CS" class="mention hashtag" rel="nofollow noopener" target="_blank">#CS</a> doing scientific computing. Home base is <a href="https://mathstodon.xyz/tags/linearalgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#linearalgebra</a> (esp <a href="https://mathstodon.xyz/tags/eigenvalues" class="mention hashtag" rel="nofollow noopener" target="_blank">#eigenvalues</a>), computational <a href="https://mathstodon.xyz/tags/mechanics" class="mention hashtag" rel="nofollow noopener" target="_blank">#mechanics</a>, <a href="https://mathstodon.xyz/tags/optimization" class="mention hashtag" rel="nofollow noopener" target="_blank">#optimization</a>. Work on numerical methods for <a href="https://mathstodon.xyz/tags/data" class="mention hashtag" rel="nofollow noopener" target="_blank">#data</a> science, formal verification of numerics, <a href="https://mathstodon.xyz/tags/plasma" class="mention hashtag" rel="nofollow noopener" target="_blank">#plasma</a> <a href="https://mathstodon.xyz/tags/physics" class="mention hashtag" rel="nofollow noopener" target="_blank">#physics</a> for <a href="https://mathstodon.xyz/tags/fusion" class="mention hashtag" rel="nofollow noopener" target="_blank">#fusion</a>, etc. I run our Center for Applied Math, am assoc dean for <a href="https://mathstodon.xyz/tags/DEI" class="mention hashtag" rel="nofollow noopener" target="_blank">#DEI</a> in the college. Collect <a href="https://mathstodon.xyz/tags/fountainpens" class="mention hashtag" rel="nofollow noopener" target="_blank">#fountainpens</a>, read <a href="https://mathstodon.xyz/tags/scifi" class="mention hashtag" rel="nofollow noopener" target="_blank">#scifi</a> & <a href="https://mathstodon.xyz/tags/fantasy" class="mention hashtag" rel="nofollow noopener" target="_blank">#fantasy</a>, <a href="https://mathstodon.xyz/tags/code" class="mention hashtag" rel="nofollow noopener" target="_blank">#code</a> in <a href="https://mathstodon.xyz/tags/julia" class="mention hashtag" rel="nofollow noopener" target="_blank">#julia</a> (and C/C++, Fortran). Also <a href="https://mathstodon.xyz/tags/coffee" class="mention hashtag" rel="nofollow noopener" target="_blank">#coffee</a>, <a href="https://mathstodon.xyz/tags/tea" class="mention hashtag" rel="nofollow noopener" target="_blank">#tea</a>, <a href="https://mathstodon.xyz/tags/books" class="mention hashtag" rel="nofollow noopener" target="_blank">#books</a>, more <a href="https://mathstodon.xyz/tags/books" class="mention hashtag" rel="nofollow noopener" target="_blank">#books</a>.

dialecticDoltAhhhh <a href="https://mathstodon.xyz/tags/Introduction" class="mention hashtag" rel="nofollow noopener" target="_blank">#Introduction</a> I'm a phd student in <a href="https://mathstodon.xyz/tags/hpc" class="mention hashtag" rel="nofollow noopener" target="_blank">#hpc</a> and <a href="https://mathstodon.xyz/tags/computationalscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#computationalscience</a>. Making computers go brrr for large scale data analytics (<a href="https://mathstodon.xyz/tags/clustering" class="mention hashtag" rel="nofollow noopener" target="_blank">#clustering</a>, nearest neighbors, <a href="https://mathstodon.xyz/tags/imageprocessing" class="mention hashtag" rel="nofollow noopener" target="_blank">#imageprocessing</a>, <a href="https://mathstodon.xyz/tags/linearalgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#linearalgebra</a>) & dabble in tools for task based <a href="https://mathstodon.xyz/tags/parallelism" class="mention hashtag" rel="nofollow noopener" target="_blank">#parallelism</a>. Hope to see some of ya'll @ <a href="https://mathstodon.xyz/tags/SC22" class="mention hashtag" rel="nofollow noopener" target="_blank">#SC22</a>! I listen to more <a href="https://mathstodon.xyz/tags/ska" class="mention hashtag" rel="nofollow noopener" target="_blank">#ska</a> than is acceptable in 2022, and will lie about having better music tastes than the digimon movie soundtrack. @ me with loose leaf <a href="https://mathstodon.xyz/tags/tea" class="mention hashtag" rel="nofollow noopener" target="_blank">#tea</a> and <a href="https://mathstodon.xyz/tags/book" class="mention hashtag" rel="nofollow noopener" target="_blank">#book</a> recs (always reading <a href="https://mathstodon.xyz/tags/fiction" class="mention hashtag" rel="nofollow noopener" target="_blank">#fiction</a> & <a href="https://mathstodon.xyz/tags/webserials" class="mention hashtag" rel="nofollow noopener" target="_blank">#webserials</a>)

au_hasardHello.I'm an associate professor at a computer science department, interested in <a href="https://mathstodon.xyz/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#statistics</a>, (also <a href="https://mathstodon.xyz/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#rstats</a>), <a href="https://mathstodon.xyz/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#bayesian</a> data analysis, <a href="https://mathstodon.xyz/tags/math" class="mention hashtag" rel="nofollow noopener" target="_blank">#math</a> (<a href="https://mathstodon.xyz/tags/linearalgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#linearalgebra</a> especially), etc.

Esa Pulkkinen<a href="https://noc.social/tags/Haskell" class="mention hashtag" rel="nofollow noopener" target="_blank">#Haskell</a> <a href="https://noc.social/tags/LinearAlgebra" class="mention hashtag" rel="nofollow noopener" target="_blank">#LinearAlgebra</a> library. <a href="https://noc.social/tags/Graph" class="mention hashtag" rel="nofollow noopener" target="_blank">#Graph</a> , <a href="https://noc.social/tags/Matrix" class="mention hashtag" rel="nofollow noopener" target="_blank">#Matrix</a> computations, <a href="https://noc.social/tags/RealNumbers" class="mention hashtag" rel="nofollow noopener" target="_blank">#RealNumbers</a>. Github: <a href="https://github.com/esapulkkinen/cifl-math-library" rel="nofollow noopener" target="_blank">https://github.com/esapulkkinen/cifl-math-library</a> Docs: <a href="https://esapulkkinen.github.io/cifl-math-library/" rel="nofollow noopener" target="_blank">https://esapulkkinen.github.io/cifl-math-library/</a>

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