Migrating to 0.3

Version 0.3 adds quadratic conic models and changes the C and Python model construction APIs. This page covers the source changes needed by existing 0.2 callers.

C API

create_qp_problem has six new trailing arguments:

qp_problem_t *create_qp_problem(
    const double *objective_c,
    const matrix_desc_t *Q_desc,
    const matrix_desc_t *R_desc,
    const matrix_desc_t *D_desc,
    const matrix_desc_t *A_desc,
    const double *con_lb,
    const double *con_ub,
    const double *var_lb,
    const double *var_ub,
    const double *objective_constant,
    int num_var_cones,
    const cone_spec_t *var_cones,
    const matrix_desc_t *affine_cone_matrix_desc,
    const double *affine_cone_offset,
    int num_affine_cones,
    const cone_spec_t *affine_cones);

An existing QP with no cones only needs the six neutral arguments appended:

qp_problem_t *problem = create_qp_problem(
    c, Q, R, D, A, con_lb, con_ub, var_lb, var_ub, objective_constant,
    0, NULL, NULL, NULL, 0, NULL);

For conic models, use cone_spec_t arrays as described in the C API overview. Variable-cone indices refer to variables; affine-cone indices refer to rows of the separately supplied affine_cone_matrix_desc (F), and affine_cone_offset has one entry per row of F. Affine cone blocks must cover every row of F.

pdhcg_postsolve now returns nonzero after a complete primal-dual recovery and zero when postsolve fails or full dual recovery is unavailable:

if (!pdhcg_postsolve(info, result, original_problem)) {
    /* Handle postsolve failure. */
}

qp_problem_t and pdhg_parameters_t gained conic fields. Do not depend on their old binary layout. Recompile downstream code and initialize parameters through set_default_parameters before overriding individual fields.

Python API

Cone metadata is now columnar. Replace a list of dictionaries with one ConeSpec:

import numpy as np
from pdhcg import ConeSpec, ConeType

cones = ConeSpec(
    types=np.array([ConeType.SOC, ConeType.POWER], dtype=np.int32),
    starts=np.array([0, 4], dtype=np.int32),
    v_dims=np.array([2, 1], dtype=np.int32),
    power_alphas=np.array([0.0, 0.4]),
)

Pass this object as variable_cones or affine_cones when constructing a Model. Legacy list[dict] inputs intentionally raise TypeError.

CVXPY support is optional:

pip install "pdhcg[cvxpy]"

Import the backend once before selecting PDHCG as the solver:

import cvxpy as cp
import pdhcg.cvxpy_backend  # Registers solver="PDHCG".

Executable Location

A source build places the command-line executable at build/pdhcg. Installed packages place it in the installation prefix's bin directory.