https://github.com/adroll/lbfgs-d

A native D implementation of Limited Memory Broyden–Fletcher–Goldfarb–Shanno optimizer
https://github.com/adroll/lbfgs-d

Last synced: 14 days ago
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A native D implementation of Limited Memory Broyden–Fletcher–Goldfarb–Shanno optimizer

• Host: GitHub
• URL: https://github.com/adroll/lbfgs-d
• Owner: AdRoll
• License: mit
• Created: 2014-11-24T23:09:50.000Z (almost 10 years ago)
• Default Branch: master
• Last Pushed: 2014-11-24T23:10:44.000Z (almost 10 years ago)
• Last Synced: 2023-05-23T20:26:35.307Z (over 1 year ago)
• Language: D
• Size: 133 KB
• Stars: 9
• Watchers: 21
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# LBFGS-D

**LBFGS-D** provides a native implementation of the Limited-Memory Broyden–Fletcher–Goldfarb–Shanno algorithm

(L-BFGS) in the **D** programming language.

The implementation is meant to be as simple and readable as possible, as a single self-contained file module.

It doesn't rely on any third-party dependency and follows closely Nocedal & Wright's algorithm description provided

in [Numerical Optimization (2 ed. Springer, 2006)](http://www.springer.com/mathematics/book/978-0-387-30303-1) book.

It is particularly well suited for cases where the function & gradient evaluations are relatively expensive compared

to the actual quasi-Newton variables updates.

The API requires you to specify a:

```D

void delegate(const float[] x, ref float[] resGrad, out float resFunc)

```

which computes at point `x` the value `resFunc` of the function to minimize,

as well as its gradient `resGrad` at point `x`.

```D

import LBFGS : LBFGS;

auto d = 30; // 30 dimensions

auto m = 5; // rank of inv-Hessian approximation

void my_function(const float[] x, ref float[] res_grad, out float res_func)

{

  ...

}

auto solution = new float[d];

solution[] = 0; // specify your starting point here

float min_val;

auto lbfgs = new LBFGS(d, &my_function, m); // will allocate memory

lbfgs.solve(solution, min_val); // run optimizer

```

Additionaly, one can specify a Jacobi preconditioner to be used.

More examples of usage can be found in the module unit-tests:

```D

rdmd -main -unittest LBFGS.d

```