CARDAL

CARDAL — Python Interface

License Python CUDA

Python bindings for CARDAL, a GPU-accelerated Augmented Lagrangian solver for large-scale semidefinite programs on a Burer-Monteiro low-rank factorization. For the CLI, distributed builds, citation, and the top-level project overview see the documentation.

The interface is intentionally small: one class (Model), one frozen dataclass (Result), one IntEnum (TerminationReason), and a solver-knob dictionary discovered via Model.default_params(). The Python build is single-GPU only — for multi-GPU runs use the C CLI under mpirun.

Installation

Requirements

Install from PyPI

pip install cardal

Optional extras:

pip install "cardal[scipy]"   # scipy >= 1.8, for scipy.sparse inputs

Install from source

git clone https://github.com/Lhongpei/CARDAL.git
cd CARDAL
pip install .

For an editable developer install:

pip install -e ".[dev]"     # pytest, editable install
pytest python/tests

During installation, pip uses scikit-build-core to compile the CUDA extension and build a local wheel. If the system nvcc is too old, point scikit-build at a newer toolchain via CUDACXX before invoking pip install:

CUDACXX=/usr/local/cuda-12.6/bin/nvcc pip install cardal

The CMake project also honours SKBUILD_CMAKE_ARGS for pass-through configuration (e.g. SKBUILD_CMAKE_ARGS="-DENABLE_MATIO=OFF").

Quick Start

From a file

CARDAL reads SDPA (.dat-s, .dat-s.gz), MATLAB (.mat), and PDSDP (.npz) files; the format is auto-detected from the header.

import cardal

m = cardal.Model.read_file("problem.dat-s")        # or .dat-s.gz / .mat / .npz

result = m.solve(
    time_sec_limit=60.0,
    eps_optimal_relative=1e-4,
    eps_primal_relative=1e-4,
    eps_dual_relative=1e-4,
    verbose=1,
)

print(f"Status:              {result.status}")
print(f"Primal objective:    {result.primal_objective:.6e}")
print(f"Rel. objective gap:  {result.rel_objective_gap:.2e}")
print(f"Outer / inner iters: {result.outer_iters} / {result.inner_iters}")

if result.status is cardal.OPTIMAL:
    print("Converged to the requested tolerances.")

print(result.summary())                            # multi-line human-readable report

Model.read_file(...) constructs and returns a loaded model. Use m.load_file(...) to replace the problem held by an existing model. Every m.solve(...) returns an independent immutable Result. SDPLIB instances such as fe4s4_sos.dat-s are available at github.com/vsdp/SDPLIB. The random-problem generators (maxcut, snl, quantum_order) are exposed only through the C CLI’s -g flag; there is no load_generator in Python. Produce a .dat-s file with the CLI first and read it back.

From NumPy arrays

Model.set_problem() accepts a list of dense numpy or scipy.sparse matrices per PSD block. As a worked example, the smallest-eigenvalue SDP for a symmetric matrix $H$ — $\min\langle H, X\rangle$ subject to $\mathrm{tr}(X)=1$, $X\succeq 0$ — whose optimum equals $\lambda_{\min}(H)$:

import numpy as np
import cardal

H = np.diag([3.0, 1.0, 2.0])                        # lambda_min(H) = 1.0

m = cardal.Model()
m.set_problem(
    block_dims=[3],                                 # one 3x3 PSD block
    C=[H],                                          # objective, one matrix per block
    A=[[np.eye(3)]],                                # 1 constraint x 1 block: trace(X) = 1
    b=[1.0],
)
result = m.solve(eps_optimal_relative=1e-6, verbose=0)
assert abs(result.primal_objective - 1.0) < 1e-3

# Post-solve metadata
print(m.num_cones, m.num_constraints, m.num_variables,
      m.block_dims, m.lp_dim, m.free_dim)

Matrices may be dense (numpy.ndarray) or any scipy.sparse matrix (CSR, CSC, and COO are all accepted). Each is assumed symmetric, and only the lower triangle is read: entries with row < col are ignored, and off-diagonals are not implicitly doubled. A[i][k] may be None to mean “constraint i touches nothing in cone k”.

From scipy.sparse

The lower-triangle convention applies verbatim to sparse inputs. Pre-mask with scipy.sparse.tril if your generator produces both triangles:

import numpy as np, scipy.sparse as sp, cardal

n = 500
# Random symmetric objective, lower triangle only (nnz counted once):
C = sp.tril((sp.random(n, n, density=1e-3, format="csr", random_state=0)
             + sp.random(n, n, density=1e-3, format="csr", random_state=1).T))
I_diag = sp.eye(n, format="csr")                    # diagonal identity is already lower-triangular

m = cardal.Model()
m.set_problem(
    block_dims=[n],
    C=[C],                                          # sparse objective
    A=[[I_diag]],                                   # one constraint: trace(X) = 1
    b=[1.0],
)
result = m.solve(time_sec_limit=30.0, verbose=1)

Mixed SDP + LP

An optional nonnegative LP tail $x_{\text{LP}}\ge 0$ is appended after the PSD blocks. Pass lp_dim, the LP objective lp_obj, and the LP-block constraint columns A_lp (dense, scipy.sparse, or COO 3-tuple (constr_ind, col_ind, val) for set_problem_coo). lp_obj is required whenever lp_dim > 0:

m.set_problem(
    block_dims=[3],
    C=[np.diag([3.0, 1.0, 2.0])],
    A=[[np.eye(3)]],
    b=[1.0],
    lp_dim=2,
    lp_obj=np.array([0.5, -0.5]),                   # linear cost on x_LP
    A_lp=np.array([[1.0, 1.0]]),                    # 1 constraint x 2 LP vars
)

Unrestricted variables

Native unrestricted variables are declared with free_dim, free_obj, and A_free. They are stored directly and are not split into positive and negative parts:

m.set_problem(
    block_dims=[],
    C=[],
    A=[[], []],
    b=[2.0, -1.0],
    free_dim=2,
    free_obj=np.array([1.0, -2.0]),
    A_free=np.array([[2.0, 0.0], [0.0, 0.5]]),
)
result = m.solve(verbose=0)
print(result.free_primal)  # [1.0, -2.0]

From raw COO triplets

For very large problems it is often faster to skip the matrix layer entirely. set_problem_coo is a 1:1 mirror of the C ABI:

m.set_problem_coo(
    block_dims=[3],
    b=[1.0],
    # C: (cone_ind, row_ind, col_ind, val) --- diag(3, 1, 2)
    C=(np.zeros(3, np.int32),
       np.arange(3, dtype=np.int32),
       np.arange(3, dtype=np.int32),
       np.array([3.0, 1.0, 2.0])),
    # A: (constr_ind, cone_ind, row_ind, col_ind, val) --- I as one constraint
    A=(np.zeros(3, np.int32),
       np.zeros(3, np.int32),
       np.arange(3, dtype=np.int32),
       np.arange(3, dtype=np.int32),
       np.ones(3)),
)

The COO path uses the same lower-triangle-only convention. LP and free-block triplets use A_lp=(constr_ind, col_ind, val) and A_free=(constr_ind, col_ind, val), respectively.

Variable Domains

CARDAL supports three native variable domains:

Second-order cones, exponential cones, and generic bounded cones are not supported.

cardal.Model

Symbol Purpose
cardal.Model() Construct an empty model. Reusable across problems.
Model.read_file(path) Construct and return a model from an auto-detected SDPA/MATLAB/PDSDP file. Raises FileNotFoundError if the path is missing and ValueError on parse failure.
m.load_file(path) Replace an existing model’s problem from a supported file.
m.set_problem(**kwargs) Build from lists of dense numpy or scipy.sparse matrices per block.
m.set_problem_coo(**kwargs) Low-level entry: build from raw COO triplet arrays (5 arrays for the constraints, 4 for the objective, plus b).
m.solve(**params) Solve the loaded problem. Returns a frozen cardal.Result. Raises RuntimeError if no problem was loaded and TypeError on unknown kwargs.
Model.default_params() @classmethod returning a fresh, mutable dict of every recognized parameter and its default.

Read-only properties (each raises RuntimeError before a problem is loaded):

Property Description
m.num_cones Number of PSD blocks $p$.
m.num_constraints Number of equality constraints $m$.
m.num_variables Total active variable count across all blocks.
m.lp_dim Size of the nonnegative LP tail (0 for pure SDP).
m.free_dim Size of the unrestricted real block.
m.block_dims Per-cone side lengths $[n_1,\dots,n_p]$, as a fresh list.

repr(m) yields "<cardal.Model (empty)>" before load and "<cardal.Model num_cones=... num_constraints=... block_dims=...>" afterwards. No silent typos. Unlike Gurobi, solve() does not accept unknown keyword arguments; any typo raises TypeError("unknown parameter '<name>'"). Cross-check current parameters against cardal.Model.default_params().keys().

Parameters

Every parameter is passed as a keyword argument to Model.solve. Model.default_params() is the canonical source of parameter keys and default values. The table below documents the commonly tuned subset. These defaults come from set_default_parameters() in src/utils.cu and are shared between the CLI and the Python API.

Parameter Type Default Description
eps_optimal_relative float 1e-4 Relative objective-gap tolerance.
eps_primal_relative float 1e-4 Relative primal residual tolerance.
eps_dual_relative float 1e-4 Relative dual residual tolerance.
time_sec_limit float 3600.0 Wall-clock budget in seconds (0.0 disables).
iteration_limit int 20000000 Total outer ALM iteration cap.
initial_rank int -1 Starting per-cone BM rank; -1 uses $\lceil 2 \log m \rceil$.
max_rank int -1 Rank cap per cone; -1 uses the Pataki bound $\lceil(\sqrt{8m+1}-1)/2\rceil$.
augmentation_mode string random Rank augmentation backend: random, qp, closed-form, or sdp.
lbfgs_history_size int 5 L-BFGS memory length used by the inner solver.
penalty_factor float 3.3 Multiplicative ALM penalty update per outer step.
initial_penalty_coef float -1.0 Starting ALM penalty $\beta$; -1.0 uses $2/\sqrt{N}$.
max_penalty_coef float 5e5 Upper cap on $\beta$.
inner_iterations_limit int 30000 Inner L-BFGS iteration cap per outer step.
l_inf_ruiz_iterations int 10 Number of L-infinity Ruiz scaling iterations; 0 disables this stage.
pock_chambolle_rescaling bool True Enable Pock-Chambolle scaling.
pock_chambolle_alpha float 1.0 Pock-Chambolle scaling exponent.
bound_objective_rescaling bool True Enable bound-objective rescaling.
psd_scale_mode string per-element PSD scaling mode: per-element or per-cone.
verbose int 2 0 silent; 1 banner + summary; 2 + iter table; 3 debug.

To disable all scaling, set l_inf_ruiz_iterations=0, pock_chambolle_rescaling=False, and bound_objective_rescaling=False. Discover every accepted Python kwarg programmatically:

defaults = cardal.Model.default_params()
print(sorted(defaults))                             # every accepted parameter name
print(defaults["eps_optimal_relative"])             # its default value

Solution Attributes

Model.solve(...) returns a frozen cardal.Result dataclass. All numeric fields are populated on every return.

Attribute Reference

Field Type Description
status TerminationReason Termination code (see below).
primal_objective float Final primal objective $\langle C, X\rangle$.
dual_objective float Final dual objective.
objective_gap float Absolute primal-dual gap.
rel_primal_residual float Relative primal feasibility residual.
rel_dual_residual float Relative dual feasibility residual.
rel_objective_gap float Relative primal-dual gap (checked against eps_optimal_relative).
runtime_sec float Solver wall-clock (excludes parse and post-solve unscaling).
outer_iters int Outer ALM iteration count.
inner_iters int Total L-BFGS inner iteration count.
num_cones int Number of cone blocks.
num_variables int Total variable count.
num_constraints int Number of equality constraints.
total_rank int Sum of per-cone BM ranks at termination.
primal_factor numpy.ndarray Flattened Burer-Monteiro factor $V$, length $\sum_c d_c r_c$.
lp_primal numpy.ndarray Nonnegative LP primal variables, length m.lp_dim.
free_primal numpy.ndarray Unrestricted primal variables, length m.free_dim.
dual numpy.ndarray Dual multipliers $y$, length num_constraints.
rank_list numpy.ndarray Per-cone final BM rank, length num_cones.

The NumPy arrays are zero-copy read-only views over C-side buffers; the underlying storage lives as long as the Result object (a private _handle field pins it).

TerminationReason is an IntEnum whose members are also re-exported at module scope:

Constant Meaning
cardal.UNSPECIFIED Solver did not set a status (should not occur on normal returns).
cardal.OPTIMAL All three relative tolerances satisfied.
cardal.TIME_LIMIT Exceeded time_sec_limit.
cardal.ITERATION_LIMIT Exceeded iteration_limit.
cardal.USER_INTERRUPT Cooperative cancel flag was set. From Python, Ctrl-C raises KeyboardInterrupt instead of returning this.

Use identity comparison (result.status is cardal.OPTIMAL) or the enum directly.

Cooperative Cancellation

Pressing Ctrl-C during a running m.solve(...) cooperatively cancels the solver and re-raises KeyboardInterrupt in Python. Mechanically:

  1. The pybind11 binding installs a SIGINT handler that flips a cooperative cancel flag (no longjmp, no abort).
  2. The outer ALM loop polls this flag at each iteration boundary. When set, the loop returns the partial state (C-side status would be USER_INTERRUPT).
  3. The Python wrapper detects the cancel and re-raises KeyboardInterrupt on the calling thread, so idiomatic try/except KeyboardInterrupt works. No Result is returned in that case.
  4. The flag is auto-cleared before the next solve(), so successive calls don’t inherit stale cancels.

Because the wrapper re-raises KeyboardInterrupt, users almost never observe result.status == cardal.USER_INTERRUPT from Python. That code exists for callers who trigger the cancel programmatically:

import threading, time
import cardal
from cardal import _core                             # private, but stable

def _cancel_after(delay):
    time.sleep(delay)
    _core.request_cancel()

m = cardal.Model.read_file("big.dat-s")
threading.Thread(target=_cancel_after, args=(5.0,), daemon=True).start()

try:
    result = m.solve(verbose=1)
except KeyboardInterrupt:
    print("cancelled from another thread")

The cancel flag is process-wide. Two solve() calls running concurrently in the same process both terminate on a single cancel; callers needing per-solve cancellation must serialize.

Troubleshooting

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