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
Eigen for matrix decomposition, included
exprtk for expression parsing, included
exprtk-custom-types for mpreal support, included

How To

If the application needs to be compiled on your machine (build the binary from source, python wheels are not available, etc.), you need to install the compiler and three libraries.

On RPM-based Linux distributions (using dnf), you need to sudo dnf install -y gcc-c++ tbb-devel mpfr-devel gmp-devel.
On DEB-based Linux distributions (using apt), you need to sudo apt install -y g++ libtbb-dev libmpfr-dev libgmp-dev.
On macOS, you need to brew install gcc tbb mpfr gmp.

If the binary is available (run the pre-compiled binary, python wheels are available, etc.), you only need the runtimes of three libraries. You can find the exact names on pkgs.org by searching tbb, gmp and mpfr.

Docker

You can simply pull the image using the following command.

docker pull tlcfem/vpmr
# or using GitHub Container Registry
docker pull ghcr.io/tlcfem/vmpr

Just use it as you would normally do with any other docker images. For example,

docker run tlcfem/vpmr -n 30

To build the image locally, use the provided Dockerfile.

wget -q https://raw.githubusercontent.com/TLCFEM/vpmr/master/https://raw.githubusercontent.com/TLCFEM/vpmr/master/resource/Dockerfile
docker build -t vpmr -f Dockerfile .

Python Package

On most platforms (Linux and macOS), wheels are available, simply 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)

Standalone Binary

All available options are:

Usage: vpmr [options]

Options:

    -n, --max-terms             <int>     number of terms (default: 10)
    -c, --max-exponent          <int>     maximum exponent (default: 4)
    -d, --precision-bits        <int>     number of precision bits (default: 512)
    -q, --quadrature-order      <int>     quadrature order (default: 500)
    -m, --precision-multiplier  <float>   precision multiplier (default: 1.05)
    -e, --tolerance             <float>   tolerance (default: 1E-8)
    -k, --kernel                <string>  file name of kernel function (default uses: exp(-t^2/4))
    -s, --singular-values                 print singular values
    -w, --weights                         print weights
    -h, --help                            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:
       terms = 30.
    exponent = 4.
   precision = 355.
 quad. order = 500.
  multiplier = 1.0500e+00.
   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=+11)...
[5/6] Solving eigen decomposition...
[6/6] Done with 11 final terms.

M = 
+2.3132817597168739e+01-2.8586221856566439e-105j
-1.1577276470339980e+01+1.2090460362812458e+01j
-1.1577276470339980e+01-1.2090460362812458e+01j
-1.5850953326980194e-01+6.1693993683365882e+00j
-1.5850953326980194e-01-6.1693993683365882e+00j
+7.3529603986874903e-01+7.5750538019498470e-01j
+7.3529603986874903e-01-7.5750538019498470e-01j
-6.6807938653881516e-02+1.0261144687127986e-02j
-6.6807938653881516e-02-1.0261144687127986e-02j
+8.8910386408859823e-04+2.2917263647765330e-04j
+8.8910386408859823e-04-2.2917263647765330e-04j
S = 
+2.0729005744773779e+00-0.0000000000000000e+00j
+2.0678104641951456e+00+5.6013828454286663e-01j
+2.0678104641951456e+00-5.6013828454286663e-01j
+2.0525989324387557e+00-1.1331825475504296e+00j
+2.0525989324387557e+00+1.1331825475504296e+00j
+2.0268330855090184e+00+1.7349515936847757e+00j
+2.0268330855090184e+00-1.7349515936847757e+00j
+1.9884211467751960e+00-2.3916428978210722e+00j
+1.9884211467751960e+00+2.3916428978210722e+00j
+1.9308660033206897e+00-3.1676394473339076e+00j
+1.9308660033206897e+00+3.1676394473339076e+00j

Running time: 1987 ms.

exp(-t^2/4)

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. Check the documentation regarding how to write a valid expression.

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

exp(-t^2/10)

Performance

The majority of the algorithm is parallelised to extract the maximum performance. The following is a typical performance profile on a i7-10750H platform using the ./vpmr -n 80.

profiling

Compilation

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 --recurse-submodules --depth 1 https://github.com/TLCFEM/vpmr.git
cd vpmr
# apply patch to enable parallel evaluation of some loops in SVD
cd eigen && git apply --ignore-space-change --ignore-whitespace ../parallelize.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++ cmake git tbb-devel mpfr-devel gmp-devel -y
git clone --recurse-submodules --depth 1 https://github.com/TLCFEM/vpmr.git
cd vpmr
cd eigen && git apply --ignore-space-change --ignore-whitespace ../parallelize.patch && cd ..
cmake -DCMAKE_BUILD_TYPE=Release .
make

Metadata

Release files for pyvpmr 260322

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for pyvpmr 260322
File	Size	Uploaded
pyvpmr-260322.tar.gz	2.7 MB	Mar 22, 2026	Details

Built distributions (wheels)

Table of built distributions (wheels) for pyvpmr 260322
File	Interpreter	ABI	Platform	Reset
pyvpmr-260322-cp314-cp314t-musllinux_1_2_x86_64.whl 4.4 MB Mar 22, 2026	CPython 3.14	CPython 3.14 free-threading	Linux musl 1.2+ x86-64	Details
pyvpmr-260322-cp314-cp314t-musllinux_1_2_aarch64.whl 4.3 MB Mar 22, 2026	CPython 3.14	CPython 3.14 free-threading	Linux musl 1.2+ ARM64	Details
pyvpmr-260322-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 3.3 MB Mar 22, 2026	CPython 3.14	CPython 3.14 free-threading	Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64	Details
pyvpmr-260322-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl 3.2 MB Mar 22, 2026	CPython 3.14	CPython 3.14 free-threading	Linux glibc 2.28+ ARM64, Linux glibc 2.26+ ARM64	Details
pyvpmr-260322-cp314-cp314-musllinux_1_2_x86_64.whl 4.4 MB Mar 22, 2026	CPython 3.14	CPython 3.14	Linux musl 1.2+ x86-64	Details
pyvpmr-260322-cp314-cp314-musllinux_1_2_aarch64.whl 4.3 MB Mar 22, 2026	CPython 3.14	CPython 3.14	Linux musl 1.2+ ARM64	Details
pyvpmr-260322-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 3.3 MB Mar 22, 2026	CPython 3.14	CPython 3.14	Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64	Details
pyvpmr-260322-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl 3.2 MB Mar 22, 2026	CPython 3.14	CPython 3.14	Linux glibc 2.28+ ARM64, Linux glibc 2.26+ ARM64	Details
pyvpmr-260322-cp313-cp313-musllinux_1_2_x86_64.whl 4.4 MB Mar 22, 2026	CPython 3.13	CPython 3.13	Linux musl 1.2+ x86-64	Details
pyvpmr-260322-cp313-cp313-musllinux_1_2_aarch64.whl 4.3 MB Mar 22, 2026	CPython 3.13	CPython 3.13	Linux musl 1.2+ ARM64	Details
pyvpmr-260322-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 3.3 MB Mar 22, 2026	CPython 3.13	CPython 3.13	Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64	Details
pyvpmr-260322-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl 3.2 MB Mar 22, 2026	CPython 3.13	CPython 3.13	Linux glibc 2.28+ ARM64, Linux glibc 2.26+ ARM64	Details
pyvpmr-260322-cp312-cp312-musllinux_1_2_x86_64.whl 4.4 MB Mar 22, 2026	CPython 3.12	CPython 3.12	Linux musl 1.2+ x86-64	Details
pyvpmr-260322-cp312-cp312-musllinux_1_2_aarch64.whl 4.3 MB Mar 22, 2026	CPython 3.12	CPython 3.12	Linux musl 1.2+ ARM64	Details
pyvpmr-260322-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 3.3 MB Mar 22, 2026	CPython 3.12	CPython 3.12	Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64	Details
pyvpmr-260322-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl 3.2 MB Mar 22, 2026	CPython 3.12	CPython 3.12	Linux glibc 2.28+ ARM64, Linux glibc 2.26+ ARM64	Details
pyvpmr-260322-cp311-cp311-musllinux_1_2_x86_64.whl 4.4 MB Mar 22, 2026	CPython 3.11	CPython 3.11	Linux musl 1.2+ x86-64	Details
pyvpmr-260322-cp311-cp311-musllinux_1_2_aarch64.whl 4.3 MB Mar 22, 2026	CPython 3.11	CPython 3.11	Linux musl 1.2+ ARM64	Details
pyvpmr-260322-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 3.3 MB Mar 22, 2026	CPython 3.11	CPython 3.11	Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64	Details
pyvpmr-260322-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl 3.2 MB Mar 22, 2026	CPython 3.11	CPython 3.11	Linux glibc 2.28+ ARM64, Linux glibc 2.26+ ARM64	Details
pyvpmr-260322-cp310-cp310-musllinux_1_2_x86_64.whl 4.4 MB Mar 22, 2026	CPython 3.10	CPython 3.10	Linux musl 1.2+ x86-64	Details
pyvpmr-260322-cp310-cp310-musllinux_1_2_aarch64.whl 4.3 MB Mar 22, 2026	CPython 3.10	CPython 3.10	Linux musl 1.2+ ARM64	Details
pyvpmr-260322-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 3.3 MB Mar 22, 2026	CPython 3.10	CPython 3.10	Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64	Details
pyvpmr-260322-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl 3.1 MB Mar 22, 2026	CPython 3.10	CPython 3.10	Linux glibc 2.28+ ARM64, Linux glibc 2.26+ ARM64	Details
pyvpmr-260322-cp39-cp39-musllinux_1_2_x86_64.whl 4.4 MB Mar 22, 2026	CPython 3.9	CPython 3.9	Linux musl 1.2+ x86-64	Details
pyvpmr-260322-cp39-cp39-musllinux_1_2_aarch64.whl 4.3 MB Mar 22, 2026	CPython 3.9	CPython 3.9	Linux musl 1.2+ ARM64	Details
pyvpmr-260322-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 3.3 MB Mar 22, 2026	CPython 3.9	CPython 3.9	Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64	Details
pyvpmr-260322-cp39-cp39-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl 3.1 MB Mar 22, 2026	CPython 3.9	CPython 3.9	Linux glibc 2.28+ ARM64, Linux glibc 2.26+ ARM64	Details
pyvpmr-260322-cp38-cp38-musllinux_1_2_x86_64.whl 4.4 MB Mar 22, 2026	CPython 3.8	CPython 3.8	Linux musl 1.2+ x86-64	Details
pyvpmr-260322-cp38-cp38-musllinux_1_2_aarch64.whl 4.3 MB Mar 22, 2026	CPython 3.8	CPython 3.8	Linux musl 1.2+ ARM64	Details
pyvpmr-260322-cp38-cp38-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 3.3 MB Mar 22, 2026	CPython 3.8	CPython 3.8	Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64	Details
pyvpmr-260322-cp38-cp38-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl 3.1 MB Mar 22, 2026	CPython 3.8	CPython 3.8	Linux glibc 2.26+ ARM64, Linux glibc 2.28+ ARM64	Details

Total release size: 124.3 MB

Release files / pyvpmr-260322.tar.gz

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Download URL	pyvpmr-260322-cp314-cp314t-musllinux_1_2_aarch64.whl
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Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

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Release files / pyvpmr-260322-cp314-cp314-musllinux_1_2_aarch64.whl

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Provenance

Identity

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Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

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Provenance

Identity

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Publishing repository: github.com/TLCFEM/vpmr
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Release files / pyvpmr-260322-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl

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Identity

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Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

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Release files / pyvpmr-260322-cp313-cp313-musllinux_1_2_x86_64.whl

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Provenance

Identity

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Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

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Provenance

Identity

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Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

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Release files / pyvpmr-260322-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl

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Identity

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Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

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Release files / pyvpmr-260322-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl

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Identity

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Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

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Identity

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Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

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Release files / pyvpmr-260322-cp312-cp312-musllinux_1_2_aarch64.whl

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Provenance

Identity

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Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

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Release files / pyvpmr-260322-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl

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Provenance

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Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

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Release files / pyvpmr-260322-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl

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Identity

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Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

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Release files / pyvpmr-260322-cp311-cp311-musllinux_1_2_x86_64.whl

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Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

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Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

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Release files / pyvpmr-260322-cp311-cp311-musllinux_1_2_aarch64.whl

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Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

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Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

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Artifact

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Release files / pyvpmr-260322-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl

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Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

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Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

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Artifact

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Release files / pyvpmr-260322-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl

Download URL	pyvpmr-260322-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
Size	3.2 MB
Tags	CPython 3.11 Linux glibc 2.26+ ARM64 Linux glibc 2.28+ ARM64
SHA-256 checksum How to use checksums	`e926e3ee8442318292527577cfad123627a2d1554c5c137c95e7b91506147661`
BLAKE2b-256 checksum How to use checksums	`48fd9c420fabc4246d9d5b5248364f1bd77d85ad4a4211fc5766e7a4e16d4cdc`
Upload date	Mar 22, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

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Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

Workflow

Publishing configuration: github.com/TLCFEM/vpmr/blob/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68/.github/workflows/wheels.yml
Publishing logs: github.com/TLCFEM/vpmr/actions/runs/23393963006/attempts/1

Artifact

Subject: pyvpmr-260322-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
SHA-256 checksum: e926e3ee8442318292527577cfad123627a2d1554c5c137c95e7b91506147661 Verifying attestations

Release files / pyvpmr-260322-cp310-cp310-musllinux_1_2_x86_64.whl

Download URL	pyvpmr-260322-cp310-cp310-musllinux_1_2_x86_64.whl
Size	4.4 MB
Tags	CPython 3.10 Linux musl 1.2+ x86-64
SHA-256 checksum How to use checksums	`4f7ab6bff46ecc9482b415e839ce6dfc7812a3a53fcae27de2342a323c992f38`
BLAKE2b-256 checksum How to use checksums	`4b53d6a5f7a7693329f0fce2a4fa1496b86104604ae45825cc881d171a95eff8`
Upload date	Mar 22, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

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Publishing platform: GitHub Actions
Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

Workflow

Publishing configuration: github.com/TLCFEM/vpmr/blob/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68/.github/workflows/wheels.yml
Publishing logs: github.com/TLCFEM/vpmr/actions/runs/23393963006/attempts/1

Artifact

Subject: pyvpmr-260322-cp310-cp310-musllinux_1_2_x86_64.whl
SHA-256 checksum: 4f7ab6bff46ecc9482b415e839ce6dfc7812a3a53fcae27de2342a323c992f38 Verifying attestations

Release files / pyvpmr-260322-cp310-cp310-musllinux_1_2_aarch64.whl

Download URL	pyvpmr-260322-cp310-cp310-musllinux_1_2_aarch64.whl
Size	4.3 MB
Tags	CPython 3.10 Linux musl 1.2+ ARM64
SHA-256 checksum How to use checksums	`66f770ed656f13b56c01461333667f654e19377993ceebefc5afec7521f08b16`
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Upload date	Mar 22, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

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Publishing platform: GitHub Actions
Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

Workflow

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Publishing logs: github.com/TLCFEM/vpmr/actions/runs/23393963006/attempts/1

Artifact

Subject: pyvpmr-260322-cp310-cp310-musllinux_1_2_aarch64.whl
SHA-256 checksum: 66f770ed656f13b56c01461333667f654e19377993ceebefc5afec7521f08b16 Verifying attestations

Release files / pyvpmr-260322-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl

Download URL	pyvpmr-260322-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Size	3.3 MB
Tags	CPython 3.10 Linux glibc 2.27+ x86-64 Linux glibc 2.28+ x86-64
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Upload date	Mar 22, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

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Publishing platform: GitHub Actions
Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

Workflow

Publishing configuration: github.com/TLCFEM/vpmr/blob/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68/.github/workflows/wheels.yml
Publishing logs: github.com/TLCFEM/vpmr/actions/runs/23393963006/attempts/1

Artifact

Subject: pyvpmr-260322-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
SHA-256 checksum: 3998dfa8ffe57f947fe85715b7f263c10de446ceb6ea0e86f31ed06001d49a07 Verifying attestations

Release files / pyvpmr-260322-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl

Download URL	pyvpmr-260322-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
Size	3.1 MB
Tags	CPython 3.10 Linux glibc 2.26+ ARM64 Linux glibc 2.28+ ARM64
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Upload date	Mar 22, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

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Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

Workflow

Publishing configuration: github.com/TLCFEM/vpmr/blob/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68/.github/workflows/wheels.yml
Publishing logs: github.com/TLCFEM/vpmr/actions/runs/23393963006/attempts/1

Artifact

Subject: pyvpmr-260322-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
SHA-256 checksum: 66d3fb9bc64b084c20a88ed665c8ab6627819b9ad27c17d8f58d2a8292009996 Verifying attestations

Release files / pyvpmr-260322-cp39-cp39-musllinux_1_2_x86_64.whl

Download URL	pyvpmr-260322-cp39-cp39-musllinux_1_2_x86_64.whl
Size	4.4 MB
Tags	CPython 3.9 Linux musl 1.2+ x86-64
SHA-256 checksum How to use checksums	`8364d41a020a5c755c784d0da2577c004ed753f03980938e6e2a79a6545b997b`
BLAKE2b-256 checksum How to use checksums	`5f348d203dee886156b475b36d48d14661c2017368a18636ba83dd3033c3edcf`
Upload date	Mar 22, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

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Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

Workflow

Publishing configuration: github.com/TLCFEM/vpmr/blob/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68/.github/workflows/wheels.yml
Publishing logs: github.com/TLCFEM/vpmr/actions/runs/23393963006/attempts/1

Artifact

Subject: pyvpmr-260322-cp39-cp39-musllinux_1_2_x86_64.whl
SHA-256 checksum: 8364d41a020a5c755c784d0da2577c004ed753f03980938e6e2a79a6545b997b Verifying attestations

Release files / pyvpmr-260322-cp39-cp39-musllinux_1_2_aarch64.whl

Download URL	pyvpmr-260322-cp39-cp39-musllinux_1_2_aarch64.whl
Size	4.3 MB
Tags	CPython 3.9 Linux musl 1.2+ ARM64
SHA-256 checksum How to use checksums	`c91aaa59a4440993010c40ef9179293a393905f68cddd832210d19b4bc02be87`
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Upload date	Mar 22, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

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Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

Workflow

Publishing configuration: github.com/TLCFEM/vpmr/blob/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68/.github/workflows/wheels.yml
Publishing logs: github.com/TLCFEM/vpmr/actions/runs/23393963006/attempts/1

Artifact

Subject: pyvpmr-260322-cp39-cp39-musllinux_1_2_aarch64.whl
SHA-256 checksum: c91aaa59a4440993010c40ef9179293a393905f68cddd832210d19b4bc02be87 Verifying attestations

Release files / pyvpmr-260322-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl

Download URL	pyvpmr-260322-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Size	3.3 MB
Tags	CPython 3.9 Linux glibc 2.27+ x86-64 Linux glibc 2.28+ x86-64
SHA-256 checksum How to use checksums	`f659075ebd184ef58e710858f361fc19c421a41e0aaaa558f7aa53659e29fad3`
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Upload date	Mar 22, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

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Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

Workflow

Publishing configuration: github.com/TLCFEM/vpmr/blob/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68/.github/workflows/wheels.yml
Publishing logs: github.com/TLCFEM/vpmr/actions/runs/23393963006/attempts/1

Artifact

Subject: pyvpmr-260322-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
SHA-256 checksum: f659075ebd184ef58e710858f361fc19c421a41e0aaaa558f7aa53659e29fad3 Verifying attestations

Release files / pyvpmr-260322-cp39-cp39-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl

Download URL	pyvpmr-260322-cp39-cp39-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
Size	3.1 MB
Tags	CPython 3.9 Linux glibc 2.26+ ARM64 Linux glibc 2.28+ ARM64
SHA-256 checksum How to use checksums	`9c204edd071366680c9de7fb72e2a58ded1219ef34cd37f27561f4f0f5970878`
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Upload date	Mar 22, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

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Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

Workflow

Publishing configuration: github.com/TLCFEM/vpmr/blob/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68/.github/workflows/wheels.yml
Publishing logs: github.com/TLCFEM/vpmr/actions/runs/23393963006/attempts/1

Artifact

Subject: pyvpmr-260322-cp39-cp39-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
SHA-256 checksum: 9c204edd071366680c9de7fb72e2a58ded1219ef34cd37f27561f4f0f5970878 Verifying attestations

Release files / pyvpmr-260322-cp38-cp38-musllinux_1_2_x86_64.whl

Download URL	pyvpmr-260322-cp38-cp38-musllinux_1_2_x86_64.whl
Size	4.4 MB
Tags	CPython 3.8 Linux musl 1.2+ x86-64
SHA-256 checksum How to use checksums	`4de458f4954a4a1788b515e83393ae41ca8740a8f6e88443bb4fefce17f1e997`
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Upload date	Mar 22, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

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Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

Workflow

Publishing configuration: github.com/TLCFEM/vpmr/blob/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68/.github/workflows/wheels.yml
Publishing logs: github.com/TLCFEM/vpmr/actions/runs/23393963006/attempts/1

Artifact

Subject: pyvpmr-260322-cp38-cp38-musllinux_1_2_x86_64.whl
SHA-256 checksum: 4de458f4954a4a1788b515e83393ae41ca8740a8f6e88443bb4fefce17f1e997 Verifying attestations

Release files / pyvpmr-260322-cp38-cp38-musllinux_1_2_aarch64.whl

Download URL	pyvpmr-260322-cp38-cp38-musllinux_1_2_aarch64.whl
Size	4.3 MB
Tags	CPython 3.8 Linux musl 1.2+ ARM64
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Upload date	Mar 22, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

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Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

Workflow

Publishing configuration: github.com/TLCFEM/vpmr/blob/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68/.github/workflows/wheels.yml
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Artifact

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SHA-256 checksum: 36ecc444d76ce690af7c83d817473bdfae2f45acdbd63a4f53a4193f57ab03a8 Verifying attestations

Release files / pyvpmr-260322-cp38-cp38-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl

Download URL	pyvpmr-260322-cp38-cp38-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Size	3.3 MB
Tags	CPython 3.8 Linux glibc 2.27+ x86-64 Linux glibc 2.28+ x86-64
SHA-256 checksum How to use checksums	`22409ec2cc3609c4f476a253b89a050ccb2fd5b9bb6fa085734d5bb628e4814a`
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Upload date	Mar 22, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

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Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

Workflow

Publishing configuration: github.com/TLCFEM/vpmr/blob/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68/.github/workflows/wheels.yml
Publishing logs: github.com/TLCFEM/vpmr/actions/runs/23393963006/attempts/1

Artifact

Subject: pyvpmr-260322-cp38-cp38-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
SHA-256 checksum: 22409ec2cc3609c4f476a253b89a050ccb2fd5b9bb6fa085734d5bb628e4814a Verifying attestations

Release files / pyvpmr-260322-cp38-cp38-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl

Download URL	pyvpmr-260322-cp38-cp38-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
Size	3.1 MB
Tags	CPython 3.8 Linux glibc 2.26+ ARM64 Linux glibc 2.28+ ARM64
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Upload date	Mar 22, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/TLCFEM/vpmr
Publishing commit: github.com/TLCFEM/vpmr/tree/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68

Workflow

Publishing configuration: github.com/TLCFEM/vpmr/blob/1ed40d5cedacb4d27f8fb15819b7a8aa9c8b9c68/.github/workflows/wheels.yml
Publishing logs: github.com/TLCFEM/vpmr/actions/runs/23393963006/attempts/1

Artifact

Subject: pyvpmr-260322-cp38-cp38-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
SHA-256 checksum: 80d43c7fcf4cce4f1fab46e58a42a675475207aa22390188da01c6a4421fa114 Verifying attestations

Release history Release notifications | RSS feed

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Sep 27, 2026 43 release files

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Mar 22, 2026 32 release files

pyvpmr 260322

VPMR C++ Implementation

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Release files / pyvpmr-260322-cp314-cp314t-musllinux_1_2_x86_64.whl

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Release files / pyvpmr-260322-cp314-cp314t-musllinux_1_2_aarch64.whl

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Release files / pyvpmr-260322-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl

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Release files / pyvpmr-260322-cp314-cp314-musllinux_1_2_x86_64.whl

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Release files / pyvpmr-260322-cp314-cp314-musllinux_1_2_aarch64.whl

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