adjoint
magicl
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adjoint | magicl | |
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11 | 14 | |
157 | 225 | |
- | 0.4% | |
0.0 | 5.4 | |
over 1 year ago | 6 months ago | |
Common Lisp | ||
- | BSD 3-clause "New" or "Revised" License |
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adjoint
- The Mathematical Hacker
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Why Is Symmetry So Important in Particle Physics?
This idea shows up in essentially all scientific fields. It’s the idea of adjointness. Together with norm, they give you the idea of fixed points, (invariants, spectra, embeddings, braids etc).I'm
Lawvere's fixed point theorem is I think the best formulation of the idea
https://ncatlab.org/nlab/show/Lawvere%27s+fixed+point+theore...
I've been putting together a brain dump on the topic
https://github.com/adamnemecek/adjoint/
Join the discord https://discord.gg/mr9TAhpyBW
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Gödel’s first incompleteness theorem – an interactive tutorial
I’ve always thought that the completeness theorem was more interesting.
Also I really like approaching this from Lawvere’s fixed point theorem. https://ncatlab.org/nlab/show/Lawvere's+fixed+point+theorem
I have been thinking about some of this stuff
https://github.com/adamnemecek/adjoint
- Young Diagrams and Classical Groups [pdf]
- Gödel, Escher, Bach: an in-depth explainer
- Transformers Seem to Mimic Parts of the Brain
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A software engineer's circuitous journey to calculate eigenvalues
Yeah I have been realizing this lately as well. I have written up a bit on this
https://github.com/adamnemecek/adjoint/
Check the raw source there’s a bunch of links.
Fixed points, diagonalizations and eigenshit are all the same thing.
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Tools for Better Thinking
I think that there are like three concepts in math or the world, the concepts being the adjoint, norm and fixed point.
https://github.com/adamnemecek/adjoint
The concept of a fixed point is so ubiquitous.
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Interactive introduction to game theory and trust
Game theory, just liek essentially everything in math, physics and probability, and cs is about adjoints, norms, and fixed points https://github.com/adamnemecek/adjoint/
Nash equilibrium is a fixed point.
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Emily Riehl is rewriting the foundations of higher category theory
For me it was the idea of adjoint functors which is the central idea of category theory.
I wrote up a bit on it here https://github.com/adamnemecek/adjoint
magicl
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A tutorial quantum interpreter in 150 lines of Lisp
(Link didn't work for me)
https://github.com/quil-lang/magicl/blob/master/src/high-lev...
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Why Lisp?
use MAGICL. [1] It is optionally and transparently accelerated by BLAS/LAPACK.
[1] https://github.com/quil-lang/magicl/blob/master/doc/high-lev...
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How fast can you multiply matrices using only common lisp?
Maybe have a look at how magicl does this?
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A software engineer's circuitous journey to calculate eigenvalues
This is essentially the first option, which is already supported by MAGICL by loading MAGICL/EXT-LAPACK [1].
[1] https://github.com/quil-lang/magicl#extensions
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Uncle Stats Wants You
I think what the magicl team has done is brilliant - allowing multiple implementations is awesome.
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Good Lisp libraries for math
Second up is magicl, especially useful if performance is a concern. This might not be as extensive as numcl, but it's been battle tested in the industry over the last decade or so. Because this uses generic functions, so long as you are using not-very-small arrays, performance should not be a concern for you. And even if you are, you could write your own functions that use the low-level functions that magicl's backends define. Otherwise performance can be at par with numpy.
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Why is python numpy *so* much faster than lisp in this example?
This Dev How-To describes (I hope in enough detail) how to add these specialized routines to MAGICL.
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CL-AUTOWRAP generated (C)BLAS wrapper in QUICKLISP
I agree... and I do don't want be the person who has not rallied. I just took a look at guicho's issue from 2019. And here, you yourself have admitted that the high level interface is less than ideal and needs more work. However, the very point that magicl is an industry standard could imply that potentially radical backward-incompatible changes can be hard. But, honestly, I want to discuss this, time permitting!
- Fast and Elegant Clojure: Idiomatic Clojure without sacrificing performance
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Anybody using Common Lisp or clojure for data science
Common Lisp is a great language to build new tools for data science, but currently has pretty awful library support existing data science workflows. Common Lisp is sorely lacking in high-quality statistics, plotting, and sparse arrays. There’s been a long work-in-progress library to bring flexible and high-performance linear algebra to Lisp, but it needs more contributors.
What are some alternatives?
excalibrain - A graph view to navigate your Obsidian vault
lisp-matrix - A matrix package for common lisp building on work by Mark Hoemmen, Evan Monroig, Tamas Papp and Rif.
obsidian-excalidraw-plugin - A plugin to edit and view Excalidraw drawings in Obsidian
py4cl - Call python from Common Lisp
Category_Theory_Machine_Learning - List of papers studying machine learning through the lens of category theory
criterium - Benchmarking library for clojure
lean-liquid - 💧 Liquid Tensor Experiment
Petalisp - Elegant High Performance Computing
milewski-ctfp-pdf - Bartosz Milewski's 'Category Theory for Programmers' unofficial PDF and LaTeX source
hash-array-mapped-trie - A hash array mapped trie implementation in c.
april - The APL programming language (a subset thereof) compiling to Common Lisp.
skiko - Kotlin MPP bindings to Skia