You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
30 lines
2.4 KiB
30 lines
2.4 KiB
2 years ago
|
# MMA and GCMMA
|
||
|
|
*A self-contained C++ implementation of MMA and GCMMA.*
|
||
|
|
|
||
|
|
[](https://travis-ci.org/jdumas/mma)
|
||
|
|
|
||
|
|
This repository contains single-file C++ implementations of MMA and GCMMA, as described in [1,2,3].
|
||
|
|
The code in this repository is based on an original code by Niels Aage, using the subproblem solver described in [4].
|
||
|
|
I made some modifications to the original code to be more C++-like (e.g. using `std::vector<>`), and slightly changed the way bounds are computed in the subproblem approximation.
|
||
|
|
I have also extended the solver to use the GCMMA method [2,3]. Both the MMA and GCMMA solvers can be found in their respective folder in `src/`.
|
||
|
|
|
||
|
|
The goal of this project is to provide a minimal, self-contained implementation of MMA and GCMMA which are easy to use and easy to integrate in an existing C++ project.
|
||
|
|
The code is still in **beta**, meaning it hasn't been tested extensively, so use at your own risks.
|
||
|
|
|
||
|
|
## Usage
|
||
|
|
|
||
|
|
This project use CMake, but you should be able to copy the files directly into your own project, since there are no dependencies.
|
||
|
|
Usage should be fairly obvious looking at the header files, but you can also take a look at the examples under the `tests/` folder.
|
||
|
|
|
||
|
|
## Other Projects
|
||
|
|
|
||
|
|
- [TopOpt_in_PETSc](https://github.com/topopt/TopOpt_in_PETSc): Multi-processor implementation of MMA using MPI and PETCs.
|
||
|
|
- [NLopt](https://nlopt.readthedocs.io/en/latest/): Open-source library for nonlinear optimization with bindings in different languages. Contains a different implementation of GCMMA.
|
||
|
|
|
||
|
|
## References
|
||
|
|
|
||
|
|
1. Svanberg, K. (1987). “The Method of Moving Asymptotes—a New Method for Structural Optimization”. International Journal for Numerical Methods in Engineering, 24(2), 359–373. https://doi.org/10.1002/nme.1620240207.
|
||
|
|
2. Svanberg, K. (2002). “A Class of Globally Convergent Optimization Methods Based on Conservative Convex Separable Approximations”. SIAM Journal on Optimization, 12(2), 555–573. https://doi.org/10.1137/s1052623499362822.
|
||
|
|
3. Svanberg, K. (2007). MMA and GCMMA - Two Methods for Nonlinear Optimization. Technical report. https://people.kth.se/~krille/mmagcmma.pdf
|
||
|
|
4. Aage, N., & Lazarov, B. S. (2013). Parallel framework for topology optimization using the method of moving asymptotes. Structural and Multidisciplinary Optimization, 47(4), 493–505. https://doi.org/10.1007/s00158-012-0869-2
|