numeric-optimization: Unified interface to various numerical optimization algorithms
Please see the README on GitHub at https://github.com/msakai/nonlinear-optimization-ad/tree/master/numeric-optimization#readme
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Flags
Manual Flags
| Name | Description | Default |
|---|---|---|
| build-examples | Build example programs | Disabled |
| with-cg-descent | Enable CGDescent optimization algorithm provided by nonlinear-optimization package and CG_DESCENT-C library. Since they are licensed under GPL, setting this flag True implies that resulting binary is also under GPL. | Disabled |
| with-lbfgs | Enable L-BFGS (since 0.1.1.0) | Enabled |
| with-lbfgsb | Enable L-BFGS-B (since 0.1.1.0) | Disabled |
Use -f <flag> to enable a flag, or -f -<flag> to disable that flag. More info
Downloads
- numeric-optimization-0.1.1.0.tar.gz [browse] (Cabal source package)
- Package description (as included in the package)
Maintainer's Corner
For package maintainers and hackage trustees
Candidates
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| Versions [RSS] | 0.1.0.0, 0.1.0.1, 0.1.1.0 |
|---|---|
| Change log | CHANGELOG.md |
| Dependencies | base (>=4.12 && <5), constraints, data-default-class (>=0.1.2.0 && <0.2), hmatrix (>=0.20.0.0), lbfgs (>=0.1 && <0.2), numeric-limits (>=0.1 && <0.2), numeric-optimization, primitive (>=0.6.4.0), vector (>=0.12.0.2 && <0.14) [details] |
| Tested with | ghc ==9.4.5, ghc ==9.2.7, ghc ==9.0.2, ghc ==8.10.7, ghc ==8.8.4, ghc ==8.6.5 |
| License | BSD-3-Clause |
| Copyright | Copyright (c) 2023 Masahiro Sakai |
| Author | Masahiro Sakai |
| Maintainer | masahiro.sakai@gmail.com |
| Category | Math, Algorithms, Optimisation, Optimization, Numeric, Numerical |
| Home page | https://github.com/msakai/nonlinear-optimization-ad#readme |
| Bug tracker | https://github.com/msakai/nonlinear-optimization-ad/issues |
| Source repo | head: git clone https://github.com/msakai/nonlinear-optimization-ad |
| Uploaded | by MasahiroSakai at 2023-06-21T14:39:20Z |
| Distributions | |
| Reverse Dependencies | 2 direct, 0 indirect [details] |
| Executables | rosenbrock |
| Downloads | 237 total (5 in the last 30 days) |
| Rating | (no votes yet) [estimated by Bayesian average] |
| Your Rating | |
| Status | Docs uploaded by user Build status unknown [no reports yet] |
Readme for numeric-optimization-0.1.1.0
[back to package description]numeric-optimization
Unified interface to various numerical optimization algorithms.
The aim of the package is to provide a convenient interface like Python's scipy.optimize.
Note that the package name is numeric-optimization and not numerical-optimization. The name numeric-optimization comes from the module name Numeric.Optimization.
Example Usage
{-# LANGUAGE OverloadedLists #-} import Data.Vector.Storable (Vector) import Numeric.Optimization main :: IO () main = do result <- minimize LBFGS def (WithGrad rosenbrock rosenbrock') [-3,-4] print (resultSuccess result) -- True print (resultSolution result) -- [0.999999999009131,0.9999999981094296] print (resultValue result) -- 1.8129771632403013e-18 -- https://en.wikipedia.org/wiki/Rosenbrock_function rosenbrock :: Vector Double -> Double rosenbrock [x,y] = sq (1 - x) + 100 * sq (y - sq x) rosenbrock' :: Vector Double -> Vector Double rosenbrock' [x,y] = [ 2 * (1 - x) * (-1) + 100 * 2 * (y - sq x) * (-2) * x , 100 * 2 * (y - sq x) ] sq :: Floating a => a -> a sq x = x ** 2 Supported Algorithms
| Algorithm | Solver implemention | Haskell binding | |
|---|---|---|---|
| CG_DESCENT | CG_DESCENT-C | nonlinear-optimization | Requires with-cg-descent flag |
| Limited memory BFGS (L-BFGS) | liblbfgs | lbfgs | |
| Limited memory BFGS with bounds constraints (L-BFGS-B) | L-BFGS-B | l-bfgs-b | Requires with-lbfgsb flag |
| Newton's method | Pure Haskell implementation using HMatrix | - |
Installation
Installing Prerequisites
BLAS and LAPACK
You may need to install BLAS and LAPACK for hmatrix.
Windows (MSYS2):
$ pacman -S mingw-w64-x86_64-lapack or if you use MSYS2 installed by stack
$ stack exec -- pacman -S mingw-w64-x86_64-lapack Debian and Ubuntu Linux:
$ apt-get install libblas-dev liblapack-dev libblas-dev and liblapack-dev are reference implementations. You need to install optimized ones for better performance. (See DebianScience/LinearAlgebraLibraries)
macOS
By default hmatrix uses BLAS and LAPACK provided by Accelerate Framework provided by macOS.
liblbfgsb
If you want to use L-BFGS-B, you have to install the development package of liblbfgsb.
Ubuntu Linux:
$ apt-get install liblbfgsb-dev Homebrew (macOS and Linux):
$ brew install msakai/tap/liblbfgsb Windows (MSYS2):
$ wget https://github.com/msakai/mingw-w64-liblbfgsb/releases/download/v3.0-1/mingw-w64-x86_64-liblbfgsb-3.0-1-any.pkg.tar.zst $ pacman -U mingw-w64-x86_64-liblbfgsb-3.0-1-any.pkg.tar.zst or if you use MSYS2 installed by stack
$ wget https://github.com/msakai/mingw-w64-liblbfgsb/releases/download/v3.0-1/mingw-w64-x86_64-liblbfgsb-3.0-1-any.pkg.tar.zst $ stack exec -- pacman -Sy $ stack exec -- pacman -U mingw-w64-x86_64-liblbfgsb-3.0-1-any.pkg.tar.zst Related Packages
- Packages for using with automatic differentiation:
- numeric-optimization-ad for using with ad package
- numeric-optimization-backprop for using with backprop package
- MIP for solving linear programming and mixed-integer linear programming problems
LICENSE
The code in thie packaged is licensed under BSD-3-Clause.
If you enable with-cg-descent flag, it uses GPL-licensed packages and the resulting binary should be distributed under GPL.