Yanked

This release has been yanked by its maintainers, and will be ignored by installers, except when explicitly specified.
Consider using release 3.3.1 instead.
Reason given by maintainers: manylinux x86_64 and Windows wheels crash the interpreter on first solve (missing MKL dispatch kernels), see cvxgrp/scs#423

scs-python

Python interface for SCS 3.0.0 and higher. The full documentation is available here.

Installation

pip install scs

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) include MKL on x86_64 Linux and Windows, and Apple Accelerate on macOS. When installing from source, 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)
pip install . -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:

Linux x86_64 wheels are built and tested against threaded MKL, and CI asserts a libiomp5 dependency on the packaged _scs_mkl extension. Windows currently falls back to sequential MKL because Intel's conda pkg-config metadata for the threaded variant is still broken.
BLAS64 is a general SCS build mode for ILP64 BLAS/LAPACK libraries, not an MKL-only feature.
For the MKL Pardiso backend specifically, BLAS64 must 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`	`10`	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. 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/

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scs 3.3.0

scs-python

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Linear solver backends

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Algorithm	Hash digest
SHA256	`6231b89575ff8de69ebc97c53a9dbc22ab34db9d5a245db9bfecc19dcf24fa66`
MD5	`f8181cef1f09a6acc8e5de4fb33b7923`
BLAKE2b-256	`3d6be897b86fdd16de171f34b53a93290bdc8c1fd4a0ea49f2f2ade60a54c937`

Algorithm	Hash digest
SHA256	`b0d45f37ff0ab648378fc7b20bf036be4fe7f9f9e39ded7e9eb99e61c5f5703a`
MD5	`13d03a4260d5340f0f902a12c8736d29`
BLAKE2b-256	`cd6e3953181dbc5514885cce02a8ad0cbf932b3d7576936371a0990faf0f586f`

Algorithm	Hash digest
SHA256	`074d53e1b4d6bfd33119684f8d807f9bdc6bb2c172a65b0af064f5ba0b2ebbab`
MD5	`e412bcdd777d62d65c0c9b0a9e9bbcea`
BLAKE2b-256	`f6c2b95d05979f101fabd04b18cda7b30e5d4fa73b7a19b436520a9b5f3ce846`

Algorithm	Hash digest
SHA256	`eeb0fbdd767680e26c9f63eb87f3826e6377d56fa2b7787e9236aeb7d984f285`
MD5	`3b7913aea2b40058c20a47c42d7cce13`
BLAKE2b-256	`2fd04776675494e5e9a22c0c0aed62497cc233d0f48e96048a0d92e2fc469c16`

Algorithm	Hash digest
SHA256	`6bcd5dc8e97c52a92294417dff94b8fd0e70801d63badeab44db438d7592d5de`
MD5	`8cfb9e5cb99641d87a6ef8b64b0e4789`
BLAKE2b-256	`5c6762bc472a0fb93a5adab7a50cca532aacb350f38cf962d23d4dc7dd2b0cc0`

Algorithm	Hash digest
SHA256	`f82b815e72e726d9ac891f39c3de8586c645c967757ad485afc1b051fb28d05c`
MD5	`2e656948aee0846679520c6a3285428e`
BLAKE2b-256	`69d0bf0d48f2d40301a600d1fd6afb67f25b89bb6e2d1b7aee9c8e0a2a7eed21`

Algorithm	Hash digest
SHA256	`5250ddf1debe185a572b9557606cea0a04cb45a8297e3f1f86d57a7e4bd37c55`
MD5	`d400d55192281f8d683c2f4503b7eb18`
BLAKE2b-256	`0640c42e471fc241d8f27ba14fe16cac44847bf75b74de34569bd95151b4efe4`

Algorithm	Hash digest
SHA256	`3c3c056685befcea3f8b8a0f3e1a80b917fc9e969f14daecf81b7adaccb9c3cc`
MD5	`f796fed6824a4a98b2bd10a67fd45c7c`
BLAKE2b-256	`448430d31480e2fea430e62e105357918d2cc8ad7d30fe37399b9a98fc714612`

Algorithm	Hash digest
SHA256	`45b71a85526d7bda4daa0255a4833a620c7830859fa2ed91ae54dfb70b3b1706`
MD5	`520a9d70aa0345fde2070ead7feb20a5`
BLAKE2b-256	`71bafa9e86fbb023513e6deb902ba2077d9359d802718bfa38131ab1e941e61d`

Algorithm	Hash digest
SHA256	`594501951fbf1e664cd34c0391c0bdd9ec5d8c0a0ef815da31d7f2c8ef8eb8fc`
MD5	`cd9f1a08ee863df84b81d41f05f6cf41`
BLAKE2b-256	`339b3ddc190297b3fba1a35887d5c4cb400073b5bed92e430ffc586a03ef3c21`