argmin
hmatrix
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argmin | hmatrix | |
---|---|---|
2 | 2 | |
887 | 378 | |
5.2% | 0.5% | |
9.2 | 3.4 | |
12 days ago | 2 months ago | |
Rust | Haskell | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
argmin
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Rust concepts I wish I learned earlier
Two things that might help Rust a lot despite the complexity is the tooling and the ecosystem. Cargo is good, the compiler is extremely helpful, and there are a lot of crates to build on for all sorts of tasks.
For example, if I need to use simulated annealing to solve an optimization problem, there already exist libraries that implement that algorithm well.[1] Unfortunately, the Haskell library for this seems to be unmaintained[2] and so does the OCaml library that I can find.[3] Similarly, Agda, Idris, and Lean 4 all seem like great languages. But not having libraries for one's tasks is a big obstacle to adoption.
Nim looks very promising. (Surprisingly so to me.) Hopefully they will succeed at gaining wider recognition and growing a healthy ecosystem.
[1] E.g., https://github.com/argmin-rs/argmin
[2] https://hackage.haskell.org/package/hmatrix-gsl-0.19.0.1 was released in 2018. (Although there are newer commits in the GitHub repo, https://github.com/haskell-numerics/hmatrix. Not too sure what is going on.)
[3] https://github.com/khigia/ocaml-anneal
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Is there a library for non-linear optimization in Rust?
You might find interest in argmin, a collection of common optimization algorithms.
hmatrix
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Rust concepts I wish I learned earlier
Two things that might help Rust a lot despite the complexity is the tooling and the ecosystem. Cargo is good, the compiler is extremely helpful, and there are a lot of crates to build on for all sorts of tasks.
For example, if I need to use simulated annealing to solve an optimization problem, there already exist libraries that implement that algorithm well.[1] Unfortunately, the Haskell library for this seems to be unmaintained[2] and so does the OCaml library that I can find.[3] Similarly, Agda, Idris, and Lean 4 all seem like great languages. But not having libraries for one's tasks is a big obstacle to adoption.
Nim looks very promising. (Surprisingly so to me.) Hopefully they will succeed at gaining wider recognition and growing a healthy ecosystem.
[1] E.g., https://github.com/argmin-rs/argmin
[2] https://hackage.haskell.org/package/hmatrix-gsl-0.19.0.1 was released in 2018. (Although there are newer commits in the GitHub repo, https://github.com/haskell-numerics/hmatrix. Not too sure what is going on.)
[3] https://github.com/khigia/ocaml-anneal
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Numpy style linear algebra
hmatrix covers the essentials; less feature-complete than python/matlab, partly because several orders of magnitudes less people use it, partly because it's meant as the "essential core".
What are some alternatives?
optimization-engine - Nonconvex embedded optimization: code generation for fast real-time optimization
hTensor - Multidimensional arrays and simple tensor computations
ceres-solver - A large scale non-linear optimization library
nimber - Finite nimber arithmetic
cmaes - A Rust implementation of the CMA-ES optimization algorithm.
hmatrix-repa - Compatability between hmatrix and repa matrices and vectors
keyboard_layout_optimizer - A keyboard layout optimizer supporting multiple layers. Implemented in Rust.
math-functions - Special mathematical functions
Peroxide - Rust numeric library with R, MATLAB & Python syntax
hmatrix-quadprogpp - bindings to quadprog++
good_lp - Linear Programming for Rust, with a user-friendly API. This crate allows modeling LP problems, and lets you solve them with various solvers.
hmatrix-nipals - Haskell library for Nonlinear Iterative Partial Least Squares method for Principal Components Analysis on large datasets