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Cleanup of the python interface #159

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Cleanup of the python interface #159

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a830bc9
add solver base class with print_stats() method
sandmaennchen Oct 18, 2023
117c5d7
cleanup examples using print_stats()
sandmaennchen Oct 18, 2023
aea593d
Added get function for lagrange multipliers
david0oo Dec 19, 2023
302414a
Removed lam_lb since the C function is not implemented yet
david0oo Dec 20, 2023
0508af9
allow to set more options
FreyJo Dec 18, 2023
75f27a1
cleanup example
sandmaennchen Oct 31, 2023
d53f673
cleanup example
sandmaennchen Oct 31, 2023
d0c21fb
cleanup example
sandmaennchen Nov 2, 2023
ac77bb5
cleanup examples
sandmaennchen Jan 9, 2024
fe0f8e9
have settable solver options as attribute
sandmaennchen Jan 10, 2024
205d851
cleanup
sandmaennchen Jan 10, 2024
4dc08f7
consistent return types (return a list whenever idx_end is given)
sandmaennchen Jan 12, 2024
652c766
minor
sandmaennchen Jan 12, 2024
2fc9d46
utils file with check for environment vars
sandmaennchen Jan 22, 2024
a7ae18b
print K only if eqs are not reduced
sandmaennchen Jan 30, 2024
c54e84b
solver as hpipm_solver
sandmaennchen Jan 30, 2024
69bdc5b
cleanup solver2
sandmaennchen Jan 30, 2024
d649e0d
fix dimension of feedback gain, add example
david0oo Jan 30, 2024
266438b
make type hints backward compatible with python 3.8
sandmaennchen Mar 27, 2024
4012910
cleanup
sandmaennchen May 16, 2024
8bef201
fix getter for matrices, numpy arrays need to be column-major
sandmaennchen May 16, 2024
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237 changes: 112 additions & 125 deletions examples/python/example_dense_qcqp_getting_started.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,130 +37,117 @@
import numpy as np
import time
import sys
import os



# check that env.sh has been run
env_run = os.getenv('ENV_RUN')
if env_run!='true':
print('ERROR: env.sh has not been sourced! Before executing this example, run:')
print('source env.sh')
sys.exit(1)

travis_run = os.getenv('TRAVIS_RUN')
#travis_run = 'true'


# define flags
warm_start = 0; # set to 1 to warm-start the primal variable


# dim
nv = 2 # number of variables
nq = 1 # number of quadratic inequality constraints


dim = hpipm_dense_qcqp_dim()

dim.set('nv', nv)
dim.set('nq', nq)

# print to shell
# dim.print_C_struct()

H = np.array([[1,0],
[0,1]])
g = np.array([[0],[0]])
Hq = np.array([[2,0],[0,2]])
gq = np.array([[-2],[-2]])
uq = -1

# qp
qcqp = hpipm_dense_qcqp(dim)
# data
qcqp.set('H', H)
qcqp.set('g', g)
qcqp.set('Hq', Hq)
qcqp.set('gq', gq)
qcqp.set('uq', uq)

# print to shell
# qp.print_C_struct()

# qp sol
qcqp_sol = hpipm_dense_qcqp_sol(dim)
# set up solver arg
#mode = 'speed_abs'
mode = 'speed'
#mode = 'balance'
#mode = 'robust'
# create and set default arg based on mode
arg = hpipm_dense_qcqp_solver_arg(dim, mode)
# create and set default arg based on mode
arg.set('mu0', 1e4)
arg.set('iter_max', 30)
arg.set('tol_stat', 1e-5)
arg.set('tol_eq', 1e-5)
arg.set('tol_ineq', 1e-5)
arg.set('tol_comp', 1e-5)
arg.set('reg_prim', 1e-12)

# if warm_start=1, then the primal variable is initialized from qp_sol
arg.set('warm_start', warm_start)
qcqp_sol.set('v', np.array([0.2929, 0.2929]))

# set up solver
solver = hpipm_dense_qcqp_solver(dim, arg)
start_time = time.time()
solver.solve(qcqp, qcqp_sol)
end_time = time.time()
if(travis_run!='true'):
print('solve time {:e}'.format(end_time-start_time))

v = qcqp_sol.get('v')
pi = qcqp_sol.get('pi')
lam_lb = qcqp_sol.get('lam_lb')
lam_ub = qcqp_sol.get('lam_ub')
lam_lg = qcqp_sol.get('lam_lg')
lam_ug = qcqp_sol.get('lam_ug')
lam_uq = qcqp_sol.get('lam_uq')
print('v = {}'.format(v.flatten()))
print('pi = {}'.format(pi.flatten()))
print('lam_lb = {}'.format(lam_lb.flatten()))
print('lam_ub = {}'.format(lam_ub.flatten()))
print('lam_lg = {}'.format(lam_lg.flatten()))
print('lam_ug = {}'.format(lam_ug.flatten()))
print('lam_uq = {}'.format(lam_uq.flatten()))
#qcqp_sol.print_C_struct()

# get solver statistics
status = solver.get('status')
res_stat = solver.get('max_res_stat')
res_eq = solver.get('max_res_eq')
res_ineq = solver.get('max_res_ineq')
res_comp = solver.get('max_res_comp')
iters = solver.get('iter')
stat = solver.get('stat')
if(travis_run!='true'):
print('\nsolver statistics:\n')
print('ipm return = {0:1d}\n'.format(status))
print('ipm max res stat = {:e}\n'.format(res_stat))
print('ipm max res eq = {:e}\n'.format(res_eq))
print('ipm max res ineq = {:e}\n'.format(res_ineq))
print('ipm max res comp = {:e}\n'.format(res_comp))
print('ipm iter = {0:1d}\n'.format(iters))
print('stat =')
print('\titer\talpha_aff\tmu_aff\t\tsigma\t\talpha_prim\talpha_dual\tmu\t\tres_stat\tres_eq\t\tres_ineq\tres_comp')
for ii in range(iters+1):
print('\t{:d}\t{:e}\t{:e}\t{:e}\t{:e}\t{:e}\t{:e}\t{:e}\t{:e}\t{:e}\t{:e}'.format(ii, stat[ii][0], stat[ii][1], stat[ii][2], stat[ii][3], stat[ii][4], stat[ii][5], stat[ii][6], stat[ii][7], stat[ii][8], stat[ii][9]))
print('')

if status==0:
print('\nsuccess!\n')
else:
print('\nSolution failed, solver returned status {0:1d}\n'.format(status))

sys.exit(int(status))
from utils import check_env, is_travis_run


def main(travis_run: bool):

# define flags
warm_start = 0; # set to 1 to warm-start the primal variable

# dim
nv = 2 # number of variables
nq = 1 # number of quadratic inequality constraints

dim = hpipm_dense_qcqp_dim()
dim.set('nv', nv)
dim.set('nq', nq)

# print to shell
# dim.print_C_struct()

H = np.eye(nv)
g = np.array([[0],[0]])
Hq = np.array([[2,0],[0,2]])
gq = np.array([[-2],[-2]])
uq = -1

# qcqp
qcqp = hpipm_dense_qcqp(dim)
qcqp.set('H', H)
qcqp.set('g', g)
qcqp.set('Hq', Hq)
qcqp.set('gq', gq)
qcqp.set('uq', uq)

# print to shell
# qp.print_C_struct()

# qcqp sol
qcqp_sol = hpipm_dense_qcqp_sol(dim)

# set up solver arg
# mode = 'speed_abs'
mode = 'speed'
# mode = 'balance'
# mode = 'robust'

# create and set default arg based on mode
arg = hpipm_dense_qcqp_solver_arg(dim, mode)

arg.set('mu0', 1e4)
arg.set('iter_max', 30)
arg.set('tol_stat', 1e-5)
arg.set('tol_eq', 1e-5)
arg.set('tol_ineq', 1e-5)
arg.set('tol_comp', 1e-5)
arg.set('reg_prim', 1e-12)

# if warm_start = 1, then the primal variable is initialized from qcqp_sol
arg.set('warm_start', warm_start)
qcqp_sol.set('v', np.array([0.2929, 0.2929]))

# set up solver
solver = hpipm_dense_qcqp_solver(dim, arg)
start_time = time.time()
solver.solve(qcqp, qcqp_sol)
end_time = time.time()

v = qcqp_sol.get('v')
pi = qcqp_sol.get('pi')
lam_lb = qcqp_sol.get('lam_lb')
lam_ub = qcqp_sol.get('lam_ub')
lam_lg = qcqp_sol.get('lam_lg')
lam_ug = qcqp_sol.get('lam_ug')
lam_uq = qcqp_sol.get('lam_uq')

print('v = {}'.format(v.flatten()))
print('pi = {}'.format(pi.flatten()))
print('lam_lb = {}'.format(lam_lb.flatten()))
print('lam_ub = {}'.format(lam_ub.flatten()))
print('lam_lg = {}'.format(lam_lg.flatten()))
print('lam_ug = {}'.format(lam_ug.flatten()))
print('lam_uq = {}'.format(lam_uq.flatten()))

# qcqp_sol.print_C_struct()

# get solver statistics
status = solver.get('status')
# res_stat = solver.get('max_res_stat')
# res_eq = solver.get('max_res_eq')
# res_ineq = solver.get('max_res_ineq')
# res_comp = solver.get('max_res_comp')
# iters = solver.get('iter')

if not travis_run:
print('solve time {:e}'.format(end_time-start_time))
solver.print_stats()

if status == 0:
print('\nsuccess!\n')
else:
print('\nSolution failed, solver returned status {0:1d}\n'.format(status))

return status



if __name__ == '__main__':

check_env()
travis_run = is_travis_run()

status = main(travis_run)

sys.exit(int(status))

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