Python

Before generating code for a parametric problem, the problem should be first specified in the setup phase. See Setup for more details.

Codegen

The code is generated by running

m.codegen(folder, **opts)

The argument folder is the name of a directory where the generated code is stored. Additional codegen options are shown in the following table

Option

Description

Allowed values
parameters
Problem parameters
'vectors' (default)
'matrices'
extension_name
Name of the generated Python module
'emosqp' (default)
Nonempty string
force_rewrite
Rewrite existing directory
False (default)
True
use_float
Use float type instead of double
False (default)
True
printing_enable
Enable printing
False (default)
True
profiling_enable
Enable profiling
False (default)
True
interrupt_enable
Enable interrupts
False (default)
True
include_codegen_src
Include codegen source code (this includes
headers/sources/Makefile in the output folder,
creating a self-contained compilable folder.
True (default)
False
prefix
Prefix for filenames and C variables,
useful if generating multiple problems.
'' (default)
string
compile
Compile the above python extension into an
importable module, allowing “import emosqp”
False (default)
True

The options are passed using named arguments, e.g.,

m.codegen('code', parameters='matrices', extension_name='myosqpext')

Extension module API

Once the code is generated, you can import a light python wrapper with

import myosqpext

where myosqpext is the extension name given in the previous section. The module imports the following functions

solve()

Solve the problem.

Returns:

tuple (x, y, status_val, iter, run_time)

  • x (ndarray) - Primal solution

  • y (ndarray) - Dual solution

  • status_val (int) - Status value as in Status values and errors

  • iter (int) - Number of iterations

  • run_time (double) - Run time

update_lin_cost(q_new)

Update linear cost.

Parameters:

q_new (ndarray) – New linear cost vector

update_lower_bound(l_new)

Update lower bound in the constraints.

Parameters:

l_new (ndarray) – New lower bound vector

update_upper_bound(u_new)

Update upper bound in the constraints.

Parameters:

u_new (ndarray) – New upper bound vector

update_bounds(l_new, u_new)

Update lower and upper bounds in the constraints.

Parameters:

  • l_new (ndarray) – New lower bound vector

  • u_new (ndarray) – New upper bound vector

If the code is generated with the option parameters set to 'matrices', the following functions are also provided

update_P(Px, Px_idx, Px_n)

Update nonzero entries of the quadratic cost matrix (only upper triangular) without changing sparsity structure.

Parameters:

  • Px (ndarray) – Values of entries to be updated

  • Px_idx (ndarray) – Indices of entries to be updated. Pass None if all the indices are to be updated

  • Px_n (int) – Number of entries to be updated. Used only if Px_idx is not None.

update_A(Ax, Ax_idx, Ax_n)

Update nonzero entries of the constraint matrix.

Parameters:

  • Ax (ndarray) – Values of entries to be updated

  • Ax_idx (ndarray) – Indices of entries to be updated. Pass None if all the indices are to be updated

  • Ax_n (int) – Number of entries to be updated. Used only if Ax_idx is not None.

update_P_A(Px, Px_idx, Px_n, Ax, Ax_idx, Ax_n)

Update nonzero entries of the quadratic cost and constraint matrices. It considers only the upper-triangular part of P.

Parameters:

  • Px (ndarray) – Values of entries to be updated

  • Px_idx (ndarray) – Indices of entries to be updated. Pass None if all the indices are to be updated

  • Px_n (int) – Number of entries to be updated. Used only if Px_idx is not None.

  • Ax (ndarray) – Values of entries to be updated

  • Ax_idx (ndarray) – Indices of entries to be updated. Pass None if all the indices are to be updated

  • Ax_n (int) – Number of entries to be updated. Used only if Ax_idx is not None.

You can update all the nonzero entries in matrix \(A\) by running

myosqpext.update_A(Ax_new, None, 0);