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(dir_name, **opts)

The argument dir_name 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
project_type Build environment
'' (default)
'Makefile'
'MinGW Makefiles'
'Unix Makefiles'
'CodeBlocks'
'Xcode'
parameters Problem parameters
'vectors' (default)
'matrices'
python_ext_name Name of the generated Python module
'emosqp' (default)
Nonempty string
force_rewrite Rewrite existing directory
False (default)
True

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

m.codegen('code', parameters='matrices', python_ext_name='emosqp')

If the project_type argument is not passed or is set to '', then no build files are generated.

Extension module API

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

import emosqp

where emosqp 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
  • 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.

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.

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

emosqp.update_A(Ax_new, None, 0);

See C/C++ Sublevel API for more details on the input arguments.