ExpmV

This is a Julia translation of the MATLAB implementation of Al-Mohy and Higham's function for computing expm(t*A)*v when A is sparse, without explicitly computing expm(A).

The original code can be found at Matlabcentral File Exchange, and the theory is explained in the following article:

Computing the Action of the Matrix Exponential, with an Application to Exponential Integrators, Awad H. Al-Mohy and Nicholas J. Higham, SIAM Journal on Scientific Computing 2011 33:2, 488-511. (preprint)

Installation

Install into Julia using the package manager:

Pkg.clone("[email protected]:marcusps/ExpmV.jl.git", "ExpmV")

Usage

expmv(t,A,v)

Benchmarks

Although both the ExpmV.jl and Expokit.jl implementations are in the early stages of development (ExpmV.jl is a direct translation of MATLAB code, and Expokit.jl is not fully optimized), here are some crude benchmarks (using Benchmark.jl) that indicate large gains over the dense expm. Source can be found in test\benchmark.jl.

Benchmark 1: Complex matrix with density of 0.0191, dimension 100, 100 trials

Row	Function	Average	Relative	Replications
1	Expmv.jl	0.0213879	5.24362	100
2	Expokit.jl	0.00407885	1.0	100
3	Julia's dense expm	0.0686303	16.8259	100

Benchmark 2: Complex matrix with density of 0.1913, dimension 100, 100 trials

Row	Function	Average	Relative	Replications
1	Expmv.jl	0.00602035	1.74028	100
2	Expokit.jl	0.00345941	1.0	100
3	Julia's dense expm	0.0275519	7.96434	100

Benchmark 3: Complex matrix with density of 0.3651, dimension 100, 100 trials

Row	Function	Average	Relative	Replications
1	Expmv.jl	0.0105414	1.97621	100
2	Expokit.jl	0.00533413	1.0	100
3	Julia's dense expm	0.0354484	6.64558	100

Clearly the current ExpmV.jl implementation needs to be looked at more carefully (See Issue #1 in particular, which appears to cause a bottle neck due to my incomplete implementation)

License

Released under the BSD 2-clause license used in Al-Mohy and Higham's original code.