sparse-approx-gsm 1.1.0

Sparse Approximation by the Generalized Soft-Min Penalty

sparse-approx-gsm

Python (+ optional CUDA) implementation of Sparse Approximation by the Generalized Soft-Min (GSM) Penalty — a sparse-recovery solver based on the Trimmed Lasso.

Solves: min ||Ax - y||_2 subject to ||x||_0 <= k

Tal Amir, Ronen Basri, Boaz Nadler — The Trimmed Lasso: Sparse Recovery Guarantees and Practical Optimization by the Generalized Soft-Min Penalty. Weizmann Institute of Science.

Install

From PyPI:

pip install sparse-approx-gsm

From source (with CUDA extension):

pip install pybind11 scikit-build-core cython numpy
NVCC=/path/to/nvcc
PYBIND11_DIR=$(python -c "import pybind11; print(pybind11.get_cmake_dir())")
CMAKE_ARGS="-DCMAKE_CUDA_COMPILER=$NVCC -Dpybind11_DIR=$PYBIND11_DIR -DCMAKE_CXX_COMPILER=/usr/bin/g++" \
  pip install --no-build-isolation . -v

From source (CPU only):

pip install --no-build-isolation .

Usage

from sparse_approx_gsm import sparse_approx_gsm, SparseApproxGSM, gsm

# High-level solver: min ||Ax - y||_2  s.t.  ||x||_0 <= k
x = sparse_approx_gsm(A, y, k, profile='normal')

# GSM function and gradient
mu, theta = gsm(z, k, gamma)

GPU Acceleration

Install CuPy for the matching CUDA version (e.g. pip install cupy-cuda12x), then pass a CuPy array to gsm() or use SparseApproxGSM(..., use_gpu=True).

When use_gpu=True, arrays are cached on the GPU and kept there throughout the solver loop, minimizing CPU-GPU data transfers.

What's New in v1.1.0

• GPU transfer optimization: Immutable arrays (A, G, y, Aty) cached on GPU; solution vector and weights stay on GPU through the MM loop
• Redundant computation elimination: GSM mu is cached from weight computation, avoiding duplicate GPU kernel launches
• Cython acceleration: Fused FISTA kernels and OpenMP-parallelized utilities for the CPU fallback path
• Module renamed: Import path is now sparse_approx_gsm (was gsm_python)