Skip to content
Code to compute solutions of minimal surface obstacle problems.
C MATLAB
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
HomogenizationExample.m
LICENSE
LinObs_PDE.m
LinObs_PDE_mex.c
LinObs_primaldual.m
LinObs_primaldual_mex.c
MinimalSurfaceObstacleExample.m
NonLinObs_L1penalty.m
NonLinObs_L1penalty_mex.c
NonLinObs_PDE.m
NonLinObs_PDE_mex.c
NonLinObs_primaldual.m
NonLinObs_primaldual_mex.c
PoissonExample.m
README.md
mexmake.m
obstacle.m

README.md

Minimal Surface Obstacle Problem Solvers

This code computes solutions of minimal surface obstacle problems using the PDE acceleration technique described in the paper

"PDE Acceleration: A convergence rate analysis and applications to obstacle problems", Jeff Calder, and Anthony Yezzi. arXiv preprint, 2018. Available here: https://arxiv.org/abs/1810.01066

All simulations presented in the above paper can be reproduced with this code. We also provide code for the primal dual method from

"An efficient primal-dual method for the obstacle problem", Dominique Zosso, Braxton Osting, Mandy Mengqi Xia, and Stanley J. Osher. Journal of Scientific Computing 73, no. 1 (2017): 416-437.

and the L1 penalty method from

"An L^1 Penalty Method for General Obstacle Problems", Giang Tran, Hayden Schaeffer, William M. Feldman, and Stanley J. Osher. SIAM Journal on Applied Mathematics 75, no. 4 (2015): 1424-1444.

All code has a pure vectorized Matlab implementation, and a faster C code implementation via the mex interface. Run the mexmake.m file to compile the mex C code.

>> mexmake.m

There are three demo scripts: PoissonExample.m, HomogenizationExample.m, and MinimalSurfaceExample.m, that show how to run the code and reproduce experiments from the paper.

You can’t perform that action at this time.