scs-python
Python interface for SCS 3.0.0 and higher. The full documentation is available here.
Installation
pip install scs
On x86-64 Linux the manylinux (glibc) wheels include the MKL Pardiso direct linear solver
(MKL linked statically into _scs_mkl, single-threaded) and use it
automatically: it is faster than the built-in QDLDL solver for most problems,
often dramatically so on larger ones, and nothing extra needs to be
installed. Intel's license notice ships in the wheel as
LICENSE-INTEL-MKL.txt. Every other wheel falls back to QDLDL.
To install from source:
git clone --recursive https://github.com/bodono/scs-python.git
cd scs-python
pip install .
Linear solver backends
SCS supports several linear solver backends. The default is AUTO, which
selects the best available solver for the platform:
- macOS: QDLDL (Apple Accelerate is available via
LinearSolver.ACCELERATE) - Linux / Windows: MKL Pardiso if available, otherwise QDLDL
# Auto-detect best backend (default)
solver = scs.SCS(data, cone)
# Explicitly select a solver
solver = scs.SCS(data, cone, linear_solver=scs.LinearSolver.QDLDL)
Available values: AUTO, QDLDL, CPU_INDIRECT, MKL, ACCELERATE,
CPU_DENSE, GPU_INDIRECT, CUDSS.
The pre-built wheels (pip install scs) link OpenBLAS on Linux and Windows,
and Apple Accelerate on macOS; the x86-64 manylinux wheels also ship the MKL
Pardiso backend (see Installation). MKL is linked statically rather than
bundled as shared libraries, whose dlopen'd CPU dispatch kernels wheel-repair
tools cannot see (cvxgrp/scs#423). The MKL backend is also available in
source builds (e.g. conda environments providing MKL), where
additional backends can be enabled with build-time flags:
# MKL Pardiso direct solver
pip install . -Csetup-args=-Dlink_mkl=true
# Use 64-bit BLAS/LAPACK integers (ILP64 / BLAS64). Requires the MKL
# backend (-Dlink_mkl=true): standard system BLAS (Accelerate/OpenBLAS)
# is LP64 and the build rejects the combination as unsafe.
pip install . -Csetup-args=-Dlink_mkl=true -Csetup-args=-Duse_blas64=true
# GPU direct solver (cuDSS)
pip install . -Csetup-args=-Dlink_cudss=true -Csetup-args=-Dint32=true
# Dense direct solver (LAPACK)
pip install . -Csetup-args=-Duse_lapack=true
# Spectral cones (logdet, nuclear norm, ell-1, sum-of-largest)
pip install . -Csetup-args=-Duse_spectral_cones=true
Notes:
- x86-64 manylinux wheels ship a
_scs_mklextension with sequential MKL linked statically (CI asserts the shipped inventory and that no wheel carries a dynamic MKL dependency). The musllinux, aarch64, macOS and Windows wheels do not include the MKL backend; Windows source builds use sequential MKL because Intel's condapkg-configmetadata for the threaded variant is still broken. - Windows wheels link conda-forge OpenBLAS pinned to 0.3.33: the win-64 0.3.34 build crashes inside its DGEMM kernels on AMD Zen 4/5 CPUs that expose AVX-512 (conda-forge/openblas-feedstock#196), so Windows source builds should avoid that build too.
BLAS64is a general SCS build mode for ILP64 BLAS/LAPACK libraries, not an MKL-only feature.- For the MKL Pardiso backend specifically,
BLAS64must be paired with 64-bit SCS integers (DLONG/int32=false), and SCS now fails early if another library in the process has already fixed MKL to an incompatible LP64/ILP64 interface layer.
Usage
import numpy as np
import scipy.sparse as sp
import scs
m, n = 4, 2
A = sp.random(m, n, density=0.5, format="csc")
b = np.random.randn(m)
c = np.random.randn(n)
P = sp.eye(n, format="csc")
cone = {"l": m} # non-negative cone
data = {"P": P, "A": A, "b": b, "c": c}
solver = scs.SCS(data, cone, verbose=False)
sol = solver.solve()
print(sol["info"]["status"]) # 'solved'
print(sol["info"]["aa_stats"]) # Anderson acceleration diagnostics
print(sol["x"]) # primal solution
Anderson acceleration tuning
SCS applies Anderson acceleration (AA) on top of ADMM. The defaults work well for most problems, but the following settings can be tuned:
| Setting | Default | Description |
|---|---|---|
acceleration_lookback |
10 |
AA memory size; 0 disables AA. |
acceleration_interval |
5 |
Apply AA every N ADMM iterations. |
acceleration_type_1 |
1 |
1 = type-I AA, 0 = type-II AA. |
acceleration_regularization |
1e-8 |
Tikhonov regularization for the AA least-squares solve; a negative value pins its absolute value. Tuned for type-I; type-II typically prefers 1e-12. |
acceleration_relaxation |
1.0 |
Relaxation factor in [0, 2]; 1.0 is vanilla AA. |
# Type-II AA with tighter regularization
solver = scs.SCS(data, cone,
acceleration_type_1=0,
acceleration_regularization=1e-12)
See the acceleration docs for the underlying algorithm.
Cone types
The cone dict supports the following keys:
| Key | Type | Description |
|---|---|---|
z |
int |
Zero cone |
l |
int |
Non-negative cone |
bu, bl |
array |
Box cone bounds |
q |
list[int] |
Second-order cone lengths |
s |
list[int] |
PSD cone matrix dimensions |
cs |
list[int] |
Complex PSD cone matrix dimensions |
ep |
int |
Primal exponential cone triples |
ed |
int |
Dual exponential cone triples |
p |
list[float] |
Power cone parameters |
With -Duse_spectral_cones=true:
| Key | Type | Description |
|---|---|---|
d |
list[int] |
Log-determinant cone matrix dimensions |
nuc_m, nuc_n |
list[int] |
Nuclear norm cone row/column dimensions |
ell1 |
list[int] |
ell-1 norm cone dimensions |
sl_n, sl_k |
list[int] |
Sum-of-largest-eigenvalues dimensions and k values |
See the cone documentation for mathematical definitions and data layout details.
Testing
pip install pytest
pytest test/
1 maintainer
Release files for scs 3.3.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| scs-3.3.1.tar.gz | 1.8 MB | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| scs-3.3.1-cp315-cp315t-win_amd64.whl | CPython 3.15 | CPython 3.15 free-threading | Windows x86-64 | Details |
| scs-3.3.1-cp315-cp315t-musllinux_1_2_x86_64.whl | CPython 3.15 | CPython 3.15 free-threading | Linux musl 1.2+ x86-64 | Details |
| scs-3.3.1-cp315-cp315t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.15 | CPython 3.15 free-threading | Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64 | Details |
| scs-3.3.1-cp315-cp315t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl | CPython 3.15 | CPython 3.15 free-threading | Linux glibc 2.27+ ARM64, Linux glibc 2.28+ ARM64 | Details |
| scs-3.3.1-cp315-cp315t-macosx_11_0_arm64.whl | CPython 3.15 | CPython 3.15 free-threading | macOS 11.0+ ARM64 | Details |
| scs-3.3.1-cp315-cp315-win_amd64.whl | CPython 3.15 | CPython 3.15 | Windows x86-64 | Details |
| scs-3.3.1-cp315-cp315-musllinux_1_2_x86_64.whl | CPython 3.15 | CPython 3.15 | Linux musl 1.2+ x86-64 | Details |
| scs-3.3.1-cp315-cp315-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.15 | CPython 3.15 | Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64 | Details |
| scs-3.3.1-cp315-cp315-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl | CPython 3.15 | CPython 3.15 | Linux glibc 2.28+ ARM64, Linux glibc 2.27+ ARM64 | Details |
| scs-3.3.1-cp315-cp315-macosx_11_0_arm64.whl | CPython 3.15 | CPython 3.15 | macOS 11.0+ ARM64 | Details |
| scs-3.3.1-cp314-cp314t-win_amd64.whl | CPython 3.14 | CPython 3.14 free-threading | Windows x86-64 | Details |
| scs-3.3.1-cp314-cp314t-musllinux_1_2_x86_64.whl | CPython 3.14 | CPython 3.14 free-threading | Linux musl 1.2+ x86-64 | Details |
| scs-3.3.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.14 | CPython 3.14 free-threading | Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 | Details |
| scs-3.3.1-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl | CPython 3.14 | CPython 3.14 free-threading | Linux glibc 2.27+ ARM64, Linux glibc 2.28+ ARM64 | Details |
| scs-3.3.1-cp314-cp314t-macosx_11_0_arm64.whl | CPython 3.14 | CPython 3.14 free-threading | macOS 11.0+ ARM64 | Details |
| scs-3.3.1-cp314-cp314-win_amd64.whl | CPython 3.14 | CPython 3.14 | Windows x86-64 | Details |
| scs-3.3.1-cp314-cp314-musllinux_1_2_x86_64.whl | CPython 3.14 | CPython 3.14 | Linux musl 1.2+ x86-64 | Details |
| scs-3.3.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.14 | CPython 3.14 | Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 | Details |
| scs-3.3.1-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl | CPython 3.14 | CPython 3.14 | Linux glibc 2.28+ ARM64, Linux glibc 2.27+ ARM64 | Details |
| scs-3.3.1-cp314-cp314-macosx_11_0_arm64.whl | CPython 3.14 | CPython 3.14 | macOS 11.0+ ARM64 | Details |
| scs-3.3.1-cp313-cp313-win_amd64.whl | CPython 3.13 | CPython 3.13 | Windows x86-64 | Details |
| scs-3.3.1-cp313-cp313-musllinux_1_2_x86_64.whl | CPython 3.13 | CPython 3.13 | Linux musl 1.2+ x86-64 | Details |
| scs-3.3.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.13 | CPython 3.13 | Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 | Details |
| scs-3.3.1-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl | CPython 3.13 | CPython 3.13 | Linux glibc 2.27+ ARM64, Linux glibc 2.28+ ARM64 | Details |
| scs-3.3.1-cp313-cp313-macosx_11_0_arm64.whl | CPython 3.13 | CPython 3.13 | macOS 11.0+ ARM64 | Details |
| scs-3.3.1-cp312-cp312-win_amd64.whl | CPython 3.12 | CPython 3.12 | Windows x86-64 | Details |
| scs-3.3.1-cp312-cp312-musllinux_1_2_x86_64.whl | CPython 3.12 | CPython 3.12 | Linux musl 1.2+ x86-64 | Details |
| scs-3.3.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.12 | CPython 3.12 | Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 | Details |
| scs-3.3.1-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl | CPython 3.12 | CPython 3.12 | Linux glibc 2.27+ ARM64, Linux glibc 2.28+ ARM64 | Details |
| scs-3.3.1-cp312-cp312-macosx_11_0_arm64.whl | CPython 3.12 | CPython 3.12 | macOS 11.0+ ARM64 | Details |
| scs-3.3.1-cp311-cp311-win_amd64.whl | CPython 3.11 | CPython 3.11 | Windows x86-64 | Details |
| scs-3.3.1-cp311-cp311-musllinux_1_2_x86_64.whl | CPython 3.11 | CPython 3.11 | Linux musl 1.2+ x86-64 | Details |
| scs-3.3.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.11 | CPython 3.11 | Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64 | Details |
| scs-3.3.1-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl | CPython 3.11 | CPython 3.11 | Linux glibc 2.27+ ARM64, Linux glibc 2.28+ ARM64 | Details |
| scs-3.3.1-cp311-cp311-macosx_11_0_arm64.whl | CPython 3.11 | CPython 3.11 | macOS 11.0+ ARM64 | Details |
| scs-3.3.1-cp310-cp310-win_amd64.whl | CPython 3.10 | CPython 3.10 | Windows x86-64 | Details |
| scs-3.3.1-cp310-cp310-musllinux_1_2_x86_64.whl | CPython 3.10 | CPython 3.10 | Linux musl 1.2+ x86-64 | Details |
| scs-3.3.1-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.10 | CPython 3.10 | Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64 | Details |
| scs-3.3.1-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl | CPython 3.10 | CPython 3.10 | Linux glibc 2.27+ ARM64, Linux glibc 2.28+ ARM64 | Details |
| scs-3.3.1-cp310-cp310-macosx_11_0_arm64.whl | CPython 3.10 | CPython 3.10 | macOS 11.0+ ARM64 | Details |
| scs-3.3.1-cp39-cp39-win_amd64.whl | CPython 3.9 | CPython 3.9 | Windows x86-64 | Details |
| scs-3.3.1-cp39-cp39-musllinux_1_2_x86_64.whl | CPython 3.9 | CPython 3.9 | Linux musl 1.2+ x86-64 | Details |
| scs-3.3.1-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.9 | CPython 3.9 | Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 | Details |
| scs-3.3.1-cp39-cp39-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl | CPython 3.9 | CPython 3.9 | Linux glibc 2.27+ ARM64, Linux glibc 2.28+ ARM64 | Details |
| scs-3.3.1-cp39-cp39-macosx_11_0_arm64.whl | CPython 3.9 | CPython 3.9 | macOS 11.0+ ARM64 | Details |
Total release size: 620.5 MB
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