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Installation

Quick Install

pip install pogs

That's it! Pre-built wheels are available for:

Platform Architectures
macOS Intel (x86_64), Apple Silicon (arm64)
Linux x86_64, aarch64
Windows x86_64

Requirements: Python 3.9+ and NumPy (installed automatically).

Verify Installation

import pogs
print(pogs.__version__)

from pogs import solve_lasso
import numpy as np

A = np.random.randn(100, 50)
b = np.random.randn(100)
result = solve_lasso(A, b, lambd=0.1)
print(f"Solved in {result['iterations']} iterations")

Optional: CVXPY Integration

To use POGS with CVXPY, install CVXPY separately:

pip install cvxpy

Then use pogs_solve():

import cvxpy as cp
from pogs import pogs_solve

x = cp.Variable(50)
prob = cp.Problem(cp.Minimize(cp.sum_squares(A @ x - b) + 0.1 * cp.norm(x, 1)))
pogs_solve(prob)  # Auto-detects Lasso, uses fast solver
print(x.value)

Building from Source

For development or if pre-built wheels aren't available:

Prerequisites

Xcode command line tools (includes Accelerate framework): 

xcode-select --install


# Ubuntu/Debian
sudo apt-get install build-essential cmake libblas-dev liblapack-dev

# Fedora/RHEL
sudo dnf install gcc-c++ cmake openblas-devel lapack-devel

Visual Studio Build Tools with C++ workload, or use conda: 

conda install -c conda-forge openblas

Install from GitHub

pip install git+https://github.com/foges/pogs.git

Local Development

git clone https://github.com/foges/pogs.git
cd pogs
pip install -e .

C++ Library

If you need the C++ library directly:

git clone https://github.com/foges/pogs.git
cd pogs
cmake -B build -DCMAKE_BUILD_TYPE=Release -DPOGS_BUILD_GPU=OFF
cmake --build build

The library will be in build/lib/.

Troubleshooting

"No matching distribution found"

Your platform may not have pre-built wheels. Build from source:

pip install git+https://github.com/foges/pogs.git

ImportError on Linux

You may need BLAS/LAPACK runtime libraries:

# Ubuntu/Debian
sudo apt-get install libopenblas0

# Fedora/RHEL
sudo dnf install openblas

Slow performance

Ensure NumPy uses optimized BLAS:

import numpy as np
np.show_config()  # Check BLAS backend

For best performance, NumPy should use OpenBLAS, MKL, or Accelerate (macOS).

Next Steps