time-dependent Hamilton-Jacobi PDEs (http://www.cs.columbia.edu/~cxz/TimeDepHJB/)
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A fast implicit method for time-dependent Hamilton-Jacobi PDEs


We present a new efficient computational approach for time-dependent first-order Hamilton-Jacobi-Bellman PDEs. Since our method is based on a time-implicit Eulerian discretization, the numerical scheme is unconditionally stable, but discretized equations for each time-slice are coupled and non-linear. We show that the same system can be re-interpreted as a discretization of a static Hamilton-Jacobi-Bellman PDE on the same physical domain. The latter was shown to be "causal", making fast (non-iterative) methods applicable. The implicit discretization results in higher computational cost per time slice compared to the explicit time marching. However, the explicit discretization is subject to a CFL-stability condition, and the implicit approach becomes significantly more efficient whenever the accuracy demands on the time-step are less restrictive than the stability. We also present a hybrid method, which aims to combine the advantages of both the explicit and implicit discretizations. We demonstrate the efficiency of our approach using several 2D examples in optimal control of isotropic fixed-horizon processes.

About the Source Code

This is a C++ implementation of the paper A fast implicit method for time-dependent Hamilton-Jacobi PDEs by Vladimirsky and Zheng.

Code Compilation

The code compilation has been tested in Linux using both gcc and Intel’s C++ compiler (icpc). If you have CMake installed, you can compile it using the following steps:

  • Create a buld directory and go into it
mkdir gcc-build && cd gcc-build
  • Run cmake to configure the Makefile
cmake ..
  • Compile the code

After compiling the code, you can run the three executables located in gcc-build/src folder. They are ExplicitTDHJ2D, ImplicitTDHJ2D and MixedTDHJ2D respectively corresponding to the explicit, implicit and hybrid methods described in the paper. Different command line options are possible. See their details by providing the -h option (e.g., run ImplicitTDHJ2D -h).

Required Library

Compiling this source code requires Boost library.

Parallel Computation

The code exploit multi-processor CPU to enable parallel compution (using OPENMP). The numerical solvers can be accelerated by parallel computation especially when the grid resolution is high. To enable parallel computation, turn on the flag USE_OPENMP in cmake.

Customizing the PDEs

The code solves the time-dependent Hamilton-Jacobi-Bellman (HJB) PDEs which have the form

$$u_t + f(x,t)|\nabla u| = g(x,t).$$

This code includes three different numerical solvers for this type of PDE, namely the explicit, implicit, and hybrid methods. The main C++ files for these solvers are ExplicitTDHJ2D.cpp, ImplicitTDHJ2D.cpp, and MixedTDHJ2D.cpp, respectively. These solvers are implemented as C++ templates, with both f and g functions as well as the boundary conditions specified as the template parameters.

Both the f and g functions are defined as C++ functionals, (i.e., a C++ struct with the operator ()). For example, a f function is defined as

struct func_F
    inline double operator() (const vector2d& pos, double t) const
    {   return ... }

And the g function is defined in a similar way (e.g., see src/HJ2DFunc.h),

struct func_G
    inline double operator() (const vector2d& pos, double t) const
    {   return ... }

The boundary condition class specifies the number of boundary nodes, the positions of the boundary nodes, and their values. Please refoer to the code in src/BoundaryCond.h for a few examples.

Once f, g and the boundary condition class are defined, you can solve the PDE by defining a test case in the solver’s main code (e.g. ImplicitTDHJ2D.cpp),

#ifdef USE_TEST_XX
typedef SimpleBoundaryCond2D                      TBC;
typedef CachedImpLattice2D<func_F, func_G, TBC>   TLattice;

Here USE_TEST_XX is the test case number. You can now enable this test case by define a flag USE_TEST_XX in src/config.h or add it in the compile flags.


Turn on the testcase 07 (which is the experiment 4 in the paper) by providing a config.h' insrc` directory

#ifndef CONFIG_INC
#   define CONFIG_INC
#define  USE_TEST_07

After compilation, run

src/ExplicitTDHJ2D -X 128 -Y 128 -T 4 -d 0.001104 -o u128_0.001104_4.npy

to solve the PDE with a 128x128 grid and a timestep size of 0.001104, using explicit method. The simulation runs backward from T=4 down to T=0.

Interpreting the Results

In the above example, the final data u(x,0) is stored in the file u128_0.001104_4.npy. This files stores the 2D array of data at time slice t=0, and the file format is Numpy's NPY format, so it can be easily read and visualized by python. For example, one can visualize the result using

../python/npy_plot.py u128_0.001104_4.npy

In addition, the folder test_scripts includes two Perl scripts as an example of testing the testcase 07 using explicit, implicit, and hybrid methods with various resolutions. In particular, test_scripts/test07.pl runs the numerical solves, and test_scripts/test07_err.pl evaluates the accuracy of different solvers by comparing with a high-resolution (3072x3072) result (using explicit method).