https://github.com/emmt/optimpack

OptimPack is a library for large optimization problems.
https://github.com/emmt/optimpack

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OptimPack is a library for large optimization problems.

• Host: GitHub
• URL: https://github.com/emmt/optimpack
• Owner: emmt
• License: other
• Created: 2014-11-09T18:36:32.000Z (over 11 years ago)
• Default Branch: master
• Last Pushed: 2025-03-20T16:04:02.000Z (about 1 year ago)
• Last Synced: 2025-03-20T16:42:02.591Z (about 1 year ago)
• Language: C
• Size: 1.35 MB
• Stars: 37
• Watchers: 6
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

          
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# OptimPack (version 3.3.1)

This is `OptimPack`, a C library for solving optimization problems. The library is mostly

targeted at very large problems (*e.g.* as the ones encountered in image restoration) but

also provide routines for problems of smaller size.

This document provides a general overview of `OptimPack`, for more specific information,

see:

- [Software installation.](./doc/INSTALL.md)

- [Developer notes.](./doc/NOTES.md)

- [Software changes and history.](./doc/CHANGES.md)

- [Solving large scale smooth problems.](./doc/LARGE_SCALE.md)

Most of the documentation is in the header files, *e.g.*

[src/optimpack.h](src/optimpack.h), in Doxygen format.

## Large scale problems

For large scale problems involving millions of variables (or even more), `OptimPack`

provides:

- several non-linear conjugate gradient (NLCG) methods (see refs. [1-3]);

- limited memory variable metric (LBFGS, see Ref. [4]) possibly with bound constraints

  and/or preconditioning (VMLMB, see Ref. [5], or BLMVM, see Ref. [6]);

- inexact monotone and nonmonotone line searches (see Ref. [7,8]);

- linear conjugate gradients [1].

The large scale optimizers of the `OptimPack` library can work with the unknowns stored in

almost any form (provided a minimal set of functions to manipulate them are implemented).

This feature may be used to exploit hardware acceleration, multi-threading or to

distribute the storage and computation across multiple machines.

See [Solving large scale smooth problems](./doc/LARGE_SCALE.md) for examples

and more information about using Optimpack to solve large scale problems.

## Problems of small to moderate size

For problems of small to moderate size, `OptimPack` provides:

- Moré & Sorensen method to compute a trust region step (see Ref. [13]);

- Mike Powell's **COBYLA** (see Ref. [10]), **NEWUOA** (see Ref. [11]), and **BOBYQA**

  (see Ref. [12]) algorithms for minimizing a function of many variables. These methods

  are *derivatives free* (only the function values are needed). **NEWUOA** is for

  unconstrained optimization. **COBYLA** accounts for general inequality constraints.

  **BOBYQA** accounts for bound constraints on the variables.

- Brent's method for the minimization of an univariate function (see Ref. [14]).

## OptimPack bindings

`OptimPack` library is written in C but, in order to make embedding OptimPack into another

language as easy as possible, most routines use **reverse communication**: all local

variables needed by the optimizers get saved into workspaces created by the library and

the optimizers never directly call the function to optimize.

The following language bindings allow `OptimPack` to be used in other programming languages:

* Directory [yorick](./yorick) contains an implementation of `OptimPack` support in

  [Yorick](https://github.com/LLNL/yorick).

* The [OptimPack.jl](https://github.com/emmt/OptimPack.jl) project implements `OptimPack`

  support for [Julia](http://julialang.org/).

* [OptimPackNextGen.jl](https://github.com/emmt/OptimPackNextGen.jl) is a new project to

  provide the same features as `OptimPack` for [Julia](http://julialang.org/) but with

  most code written in pure Julia. For now, pure Julia version of the methods devoted to

  large scale problems are available. To use Powell algorithms (devoted to moderate size

  problems) the dynamic libraries of `OptimPack` are still needed.

* The [TiPi](https://github.com/emmt/TiPi) project provides a framework for solving

  inverse image reconstruction problems in **Java**. The optimization package of `TiPi` is

  a Java version of the `OptimPack` library.

## References

1. M.R. Hestenes & E. Stiefel, "*Methods of Conjugate Gradients for Solving Linear

   Systems*," Journal of Research of the National Bureau of Standards 49, pp. 409-436

   (1952).

2. W.W. Hager & H. Zhang, "*A survey of nonlinear conjugate gradient methods*," Pacific

   Journal of Optimization, Vol. 2, pp. 35-58 (2006).

3. W.W. Hager & H. Zhang, "*A New Conjugate Gradient Method with Guaranteed Descent and an

   Efficient Line Search*," SIAM J. Optim., Vol. 16, pp. 170-192 (2005).

4. D. Liu and J. Nocedal, "*On the limited memory BFGS method for large scale

   optimization*", Mathematical Programming B **45**, 503-528 (1989).

5. É. Thiébaut, "*Optimization issues in blind deconvolution algorithms*," in Astronomical

   Data Analysis II, SPIE Proc. **4847**, 174-183 (2002).

6. S.J. Benson & J.J. Moré, "*A limited memory variable metric method in subspaces and

   bound constrained optimization problems*", in Subspaces and Bound Constrained

   Optimization Problems, (2001).

7. E.G. Birgin, J.M. Martínez & M. Raydan, "*Nonmonotone Spectral Projected Gradient

   Methods on Convex Sets*," SIAM J. Optim. **10**, 1196-1211 (2000).

8. Jorge J. Moré and David J. Thuente, "*Line search algorithms with guaranteed sufficient

   decrease*" in ACM Transactions on Mathematical Software (TOMS) Volume 20, Issue 3,

   Pages 286-307 (1994).

9. T. Steihaug, "*The conjugate gradient method and trust regions in large scale

   optimization*", SIAM Journal on Numerical Analysis, vol. **20**, pp. 626-637 (1983).

10. M.J.D. Powell, "*A direct search optimization method that models the objective and

    constraint functions by linear interpolation*," in Advances in Optimization and

    Numerical Analysis Mathematics and Its Applications, vol. **275** (eds. Susana Gomez

    and Jean-Pierre Hennart), Kluwer Academic Publishers, pp. 51-67 (1994).

11. M.J.D. Powell, "*The NEWUOA software for unconstrained minimization without

    derivatives*", in Large-Scale Nonlinear Optimization, editors G. Di Pillo and M. Roma,

    Springer, pp. 255-297 (2006).

12. M.J.D. Powell, "*The BOBYQA Algorithm for Bound Constrained Optimization Without

    Derivatives*." Technical report, Department of Applied Mathematics and Theoretical

    Physics, University of Cambridge (2009).

13. J.J. Moré & D.C. Sorensen, "*Computing A Trust Region Step*," SIAM J. Sci. Stat. Comp.

    **4**, 553-572 (1983).

14. R.P. Brent, "*Algorithms for Minimization without Derivatives*," Prentice-Hall, Inc.

    (1973).

## Authors

* Éric Thiébaut (https://github.com/emmt)

## Credits

From 2009 to 2014, the development of `OptimPack` was supported by the

[MiTiV](http://mitiv-univ-lyon1.fr) project funded by the French [*Agence Nationale pour

la Recherche*](http://www.agence-nationale-recherche.fr) (Ref. ANR-09-EMER-008).

A simpler version of `OptimPack` is provided by

[OptimPackLegacy](https://github.com/emmt/OptimPackLegacy) project.

## License

The `OptimPack` library is released under the [MIT "Expat" License](./LICENSE.md).