The VPMR Algorithm
Project description
VPMR C++ Implementation
Call For Help
- more performant parallel SVD algorithm:
eigen
only provides sequential SVD - alternative integration: currently only Gauss-Legendre quadrature is available
What Is This?
This is a C++ implementation of the VPMR algorithm to compute the approximation of arbitrary smooth kernel. A Python package is also provided.
Check the reference paper 10.1007/s10915-022-01999-1 and the original MATLAB implementation for more details.
In short, the algorithm tries to find a summation of exponentials to approximate a given kernel function. In mathematical terms, it looks for a set of $m_j$ and $s_j$ such that
$$ \max_{t\in{}I}\left|g(t)-\sum_jm_j\exp(-s_jt)\right|<\epsilon. $$
In the above, $g(t)$ is the given kernel function and $\epsilon$ is the prescribed tolerance.
Dependency
The following libraries are required:
- gmp for multiple precision arithmetic
- mpfr for multiple-precision floating-point computations
- tbb for parallel computing
The following libraries are included:
- mpreal
mpreal
type C++ wrapper, included - BigInt
BigInt
arbitrary large integer for combinatorial number, included - Eigen for matrix decomposition, included
- exprtk for expression parsing, included
- exprtk-custom-types for
mpreal
support, included
How To
Python Package
[!WARNING] The Python module needs external libraries to be installed.
[!WARNING] Windows users need to have a working MSYS2 environment. See below for more details. For other environments, you need to figure out how to install
gmp
andmpfr
on your own.
On RPM-based Linux distributions (using dnf
), if you are:
- compiling the application from source (or wheels are not
available),
sudo dnf install -y gcc-c++ tbb-devel mpfr-devel gmp-devel
- using the packaged binary (wheels are available),
sudo dnf install -y gmp mpfr tbb
On DEB-based Linux distributions (using apt
), you need to sudo apt install -y libtbb-dev libmpfr-dev libgmp-dev
.
On macOS, you need to brew install tbb mpfr gmp
.
Then install the package with pip
.
pip install pyvpmr
If the corresponding wheel is not available, the package will be compiled, which takes a few minutes.
The execution of the algorithm always requires available gmp
, mpfr
and tbb
libraries.
Jumpstart
import numpy as np
from pyvpmr import vpmr, plot
def kernel(x):
return np.exp(-x ** 2 / 4)
if __name__ == '__main__':
m, s = vpmr(n=50, k='exp(-t^2/4)')
plot(m, s, kernel)
Compile Binary
[!WARNING] The application relies on
eigen
andexprtk
, which depend on very heavy usage of templates. The compilation would take minutes and around 2 GB memory. You need to install librariesgmp
,mpfr
andtbb
before compiling.
Docker
To avoid the hassle of installing dependencies, you can use the provided Dockerfile
.
For example,
wget -q https://raw.githubusercontent.com/TLCFEM/vpmr/master/Dockerfile
docker build -t vpmr -f Dockerfile .
Or you simply pull using the following command.
docker pull tlcfem/vpmr
Windows
Use the following instructions based on MSYS2, or follow the Linux instructions below with WSL.
# install necessary packages
pacman -S git mingw-w64-x86_64-cmake mingw-w64-x86_64-tbb mingw-w64-x86_64-gcc mingw-w64-x86_64-ninja mingw-w64-x86_64-gmp mingw-w64-x86_64-mpfr
# clone the repository
git clone --depth 1 https://github.com/TLCFEM/vpmr.git
# initialise submodules
cd vpmr
git submodule update --init --recursive
# apply patch to enable parallel evaluation of some loops in SVD
cd eigen && git apply --ignore-space-change --ignore-whitespace ../patch_size.patch && cd ..
# configure and compile
cmake -G Ninja -DCMAKE_BUILD_TYPE=Release .
ninja
Linux
The following is based on Fedora.
sudo dnf install gcc g++ gfortran cmake git -y
sudo dnf install tbb-devel mpfr-devel gmp-devel -y
git clone --depth 1 https://github.com/TLCFEM/vpmr.git
cd vpmr
git submodule update --init --recursive
cd eigen && git apply --ignore-space-change --ignore-whitespace ../patch_size.patch && cd ..
cmake -DCMAKE_BUILD_TYPE=Release .
make
Usage
All available options are:
Usage: vpmr [options]
Options:
-n <int> number of terms (default: 10)
-d <int> number of precision bits (default: 512)
-q <int> quadrature order (default: 500)
-m <float> precision multiplier (default, minimum: 1.5)
-nc <int> controls the maximum exponent (default: 4)
-e <float> tolerance (default: 1E-8)
-k <string> file name of kernel function (default: exp(-t^2/4))
-s print singular values
-w print weights
-h print this help message
The minimum required precision can be estimated by the parameter $n$. The algorithm involves the computation of $C(4n,k)$ and $2^{4n}$. The number of precision bits shall be at least $4n+\log_2C(4n,2n)$. In the implementation, this number will be further multiplied by the parameter $m$.
Example
The default kernel is exp(-t^2/4)
. One can run the application with the following command:
./vpmr -n 30
The output is:
Using the following parameters:
nc = 4.
n = 30.
order = 500.
precision = 336.
tolerance = 1.0000e-08.
kernel = exp(-t*t/4).
[1/6] Computing weights... [60/60]
[2/6] Solving Lyapunov equation...
[3/6] Solving SVD...
[4/6] Transforming (P=+9)...
[5/6] Solving eigen decomposition...
[6/6] Done.
M =
+1.1745193571738943e+01-1.4261645574068720e-100j
-5.5143304351134397e+00+5.7204056791636839e+00j
-5.5143304351134397e+00-5.7204056791636839e+00j
-1.6161617424833762e-02+2.3459542440459513e+00j
-1.6161617424833762e-02-2.3459542440459513e+00j
+1.6338578576177487e-01+1.9308431539218418e-01j
+1.6338578576177487e-01-1.9308431539218418e-01j
-5.4905134221689715e-03+2.2104939243740062e-03j
-5.4905134221689715e-03-2.2104939243740062e-03j
S =
+1.8757961592204051e+00-0.0000000000000000e+00j
+1.8700580506914817e+00+6.2013413918954552e-01j
+1.8700580506914817e+00-6.2013413918954552e-01j
+1.8521958553280000e+00-1.2601975249082220e+00j
+1.8521958553280000e+00+1.2601975249082220e+00j
+1.8197653300065935e+00+1.9494562062795735e+00j
+1.8197653300065935e+00-1.9494562062795735e+00j
+1.7655956664692953e+00-2.7555720406099038e+00j
+1.7655956664692953e+00+2.7555720406099038e+00j
Running time: 3 s.
Arbitrary Kernel
For arbitrary kernel, it is necessary to provide the kernel function in a text file.
The file should contain the kernel expressed as a function of variable t
.
The exprtk
is used to parse the expression and compute the value.
The provided kernel function must be valid and supported by exprtk
.
For example, to compute the approximation of exp(-t^2/10)
, one can create a file kernel.txt
with the following
content:
exp(-t*t/10)
In the following, the kernel function is echoed to a file and then used as an input to the application.
echo "exp(-t*t/10)" > kernel.txt
./vpmr -n 60 -k kernel.txt -e 1e-12
Binary
The binary requires available gmp
, mpfr
and tbb
libraries.
❯ ldd vpmr
linux-vdso.so.1 (0x00007ffcf3121000)
libgmp.so.10 => /lib64/libgmp.so.10 (0x00007f72087e8000)
libmpfr.so.6 => /lib64/libmpfr.so.6 (0x00007f7208736000)
libtbb.so.2 => /lib64/libtbb.so.2 (0x00007f72086f2000)
libstdc++.so.6 => /lib64/libstdc++.so.6 (0x00007f7208400000)
libm.so.6 => /lib64/libm.so.6 (0x00007f7208320000)
libgcc_s.so.1 => /lib64/libgcc_s.so.1 (0x00007f72086d0000)
libc.so.6 => /lib64/libc.so.6 (0x00007f7208143000)
/lib64/ld-linux-x86-64.so.2 (0x00007f72088a1000)
The distributed appimage
is portable.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distributions
Hashes for pyvpmr-240916-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9981b6aaf2ec186e8ab0ba43bc162c471b8727db54b50e8b1ecd0c3d2ba6f00a |
|
MD5 | 18cc014e51a5e49bf65965eb7833f877 |
|
BLAKE2b-256 | f826debddb014f5809d5386e8dd5666830f94396e8ffcdbbf9c68ed87a6bcccb |
Hashes for pyvpmr-240916-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3824a120e26a22d10910a7d69b2dc34641ca11fab2b34b77d813e998c694f01b |
|
MD5 | 76a8e1702ef232e46acc49f052a0963b |
|
BLAKE2b-256 | 0b796e0548bae68bda0a01f94798cceaf67668128678047d1517cb2fd17e4c99 |
Hashes for pyvpmr-240916-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1979be61c1802a34f1cbf22849f4b04f95a828e55fa8e116b01da237b929b049 |
|
MD5 | 13ae63c4d7a054698f4444f69eef87ba |
|
BLAKE2b-256 | fb0f2148dd9196e3371c5f127872dd0f3ff7235222b8a7cb0fc529cc37ddd71c |
Hashes for pyvpmr-240916-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 97098c862b18b8698284b0c8f79ec0d81e7cdbffe47f21d2b4b5064b88bb2989 |
|
MD5 | 62813ca67c5ff63243dc16d032958815 |
|
BLAKE2b-256 | f80decc1e7ff7c7d760b4459493380042288a78e46f3ef68f143bfddb5e8beda |
Hashes for pyvpmr-240916-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 04d533b06cc1197564a3b177bcc65f566a7c160f999eb9eb9b6fbf6216e8436d |
|
MD5 | 8fe893193c1897a6aac752d52a52e1a5 |
|
BLAKE2b-256 | 806ad33f1c9fb7a0e30d992e38152e4b1917586690297d9ec7f16b5d208ef214 |
Hashes for pyvpmr-240916-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 53da42bb3067b3e3a5fcd30ac52dacc89739c68301061d9f44a94370d89f19ce |
|
MD5 | 2d47ccb947be59496845244fdee50c66 |
|
BLAKE2b-256 | 0f8547595bdbc5a68b472a1df372cca61dca1d25373f7b68b2c19febf32f3cdd |