# 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 (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

emosqp.update_A(Ax_new, None, 0);


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