def lcp_generic(id, z, w):
options = sn.SolverOptions(id)
info = sn.linearComplementarity_driver(lcp, z, w, options)
print(' iter =', options.iparam[sn.SICONOS_IPARAM_ITER_DONE])
print(' error=', options.dparam[sn.SICONOS_DPARAM_RESIDU])
return info
def lemke_optimizer(eco, payoff_matrix=None, dirac_mode=True):
A = np.zeros((eco.populations.size, eco.populations.size * eco.layers))
for k in range(eco.populations.size):
A[k, k * eco.layers:(k + 1) * eco.layers] = -1
q = np.zeros(eco.populations.size + eco.populations.size * eco.layers)
q[eco.populations.size * eco.layers:] = -1
q = q.reshape(-1, 1)
if payoff_matrix is None:
payoff_matrix = total_payoff_matrix_builder(eco)
H = np.block([[-payoff_matrix, A.T],
[-A, np.zeros((A.shape[0], eco.populations.size))]])
lcp = sn.LCP(H, q)
ztol = 1e-8
#solvers = [sn.SICONOS_LCP_PGS, sn.SICONOS_LCP_QP,
# sn.SICONOS_LCP_LEMKE, sn.SICONOS_LCP_ENUM]
z = np.zeros((eco.layers * 2 + eco.populations.size, ), np.float64)
w = np.zeros_like(z)
options = sn.SolverOptions(sn.SICONOS_LCP_PIVOT)
#sn.SICONOS_IPARAM_MAX_ITER = 10000000
#options.iparam[sn.SICONOS_IPARAM_MAX_ITER] = 1000000
options.dparam[sn.SICONOS_DPARAM_TOL] = 10**(-5)
info = sn.linearComplementarity_driver(lcp, z, w, options)
if sn.lcp_compute_error(lcp, z, w, ztol) > 10**(-5):
print(sn.lcp_compute_error(lcp, z, w, ztol), "Error")
#print(info, "Info")
return z
def test_lcp_pgs():
SO = N.SolverOptions(lcp, N.SICONOS_LCP_PGS)
info = N.lcp_pgs(lcp, z, w, SO)
print('pgs iter =', SO.iparam[1])
print('pgs error=', SO.dparam[1])
assert (np.linalg.norm(z - zsol) <= ztol)
assert not info
def test_fc3dnsgs():
N.setNumericsVerbose(2)
FCP = N.FrictionContactProblem(3,M,q,mu)
SO=N.SolverOptions(N.SICONOS_FRICTION_3D_NSGS)
r=N.fc3d_nsgs(FCP, reactions, velocities, SO)
assert SO.dparam[1] < 1e-10
assert not r
def test_fc3dlocalac():
N.setNumericsVerbose(2)
FCP = N.FrictionContactProblem(3,M,q,mu)
SO=N.SolverOptions(N.SICONOS_FRICTION_3D_NSN_AC)
r = N.fc3d_nonsmooth_Newton_AlartCurnier(FCP, reactions, velocities, SO)
assert SO.dparam[1] < 1e-10
assert not r
def test_fc3dfischer():
N.setNumericsVerbose(2)
FCP = N.FrictionContactProblem(3,M,q,mu)
SO=N.SolverOptions(N.SICONOS_FRICTION_3D_NSN_FB)
r = N.fc3d_nonsmooth_Newton_FischerBurmeister(FCP, reactions, velocities, SO)
assert SO.dparam[1] < 1e-10
assert not r
def solve_reduced(fcp, solver_reduced):
SO_reduced = sn.SolverOptions(solver_reduced)
SO_reduced.dparam[0] = np.sqrt(fcp.numberOfContacts) * 1e-9
size_reaction = fcp.numberOfContacts * 3
reaction_reduced = np.zeros((size_reaction, ))
velocities_reduced = np.zeros((size_reaction, ))
return sn.fc3d_driver(fcp, reaction_reduced, velocities_reduced,
SO_reduced, no)
def test_ncp_newton_FBLSA():
ncp = SN.NCP(2, ncp_function, ncp_Nablafunction)
z = np.array([0., 0.])
w = np.array([0., 0.])
SO = SN.SolverOptions(SN.SICONOS_NCP_NEWTON_FB_FBLSA)
info = SN.ncp_driver(ncp, z, w, SO)
assert (np.linalg.norm(z - zsol) <= ztol)
assert not info
def solve_global(fcp, solver):
SO = sn.SolverOptions(solver)
SO.dparam[0] = np.sqrt(fcp.numberOfContacts) * 1e-9
n, m = fcp.H.shape
size_reaction = m
reaction = np.zeros((size_reaction, ))
velocities = np.zeros((size_reaction, ))
global_velocities = np.zeros((n, ))
return sn.gfc3d_driver(fcp, reaction, velocities, global_velocities, SO)
def test_mcp_newton_FB_FBLSA_2():
mcp = SN.MCP(n - 5, 5, mcp_function_2, mcp_Nablafunction_2)
z = np.zeros(n)
w = np.zeros(n)
SO = SN.SolverOptions(mcp, SN.SICONOS_MCP_NEWTON_FB_FBLSA)
info = SN.mcp_newton_FB_FBLSA(mcp, z, w, SO)
print("z = ", z)
print("w = ", w)
#assert (np.linalg.norm(z-zsol) <= ztol)
assert not info
def test_mcp_newton_min_FBLSA():
mcp = sn.MCP(0, 2, mcp_function, mcp_Nablafunction)
z = np.array([0., 0.])
w = np.array([0., 0.])
SO = sn.SolverOptions(sn.SICONOS_MCP_NEWTON_MIN_FBLSA)
info = sn.mcp_newton_min_FBLSA(mcp, z, w, SO)
print("z = ", z)
print("w = ", w)
assert np.linalg.norm(z - zsol) <= ztol
assert not info
def test_mcp_newton_minFBLSA():
mcp = SN.MixedComplementarityProblem2(0, 2, mcp_function, mcp_Nablafunction)
z = np.array([0., 0.])
w = np.array([0., 0.])
SO = SN.SolverOptions(mcp, SN.SICONOS_MCP_NEWTON_MINFBLSA)
info = SN.mcp_newton_minFBLSA(mcp, z, w, SO)
#print("z = ", z)
#print("w = ", w)
assert (np.linalg.norm(z-zsol) <= ztol)
assert not info
def test_mcp_FB_2():
mcp = sn.MCP_old(n - 3, 3, mcp_function_2, mcp_Nablafunction_2)
z = np.zeros(n)
w = np.zeros(n)
options = sn.SolverOptions(sn.SICONOS_MCP_OLD_FB)
sn.mcp_old_driver_init(mcp, options)
info = sn.mcp_old_FischerBurmeister(mcp, z, w, options)
sn.mcp_old_driver_reset(mcp, options)
print("z = ", z)
print("w = ", w)
assert not info
def test_mcp_FB_2():
mcp = N.MCP_old(n - 3, 3, mcp_function_2, mcp_Nablafunction_2)
z = np.zeros(n)
w = np.zeros(n)
SO = N.SolverOptions(mcp, N.SICONOS_MCP_OLD_FB)
N.mcp_old_driver_init(mcp, SO)
info = N.mcp_old_FischerBurmeister(mcp, z, w, SO)
N.mcp_old_driver_reset(mcp, SO)
print("z = ", z)
print("w = ", w)
assert not info
def test_mlcp_enum_large():
z = np.array([0., 0., 0., 0., 0., 0., 0.])
w = np.array([0., 0., 0., 0., 0., 0., 0.])
mlcp, zsol = createMLCP_large()
SO = sn.SolverOptions(sn.SICONOS_MLCP_ENUM)
sn.mlcp_driver_init(mlcp, SO)
info = sn.mlcp_enum(mlcp, z, w, SO)
sn.mlcp_driver_reset(mlcp, SO)
print("z = ", z)
print("w = ", w)
assert (np.linalg.norm(z - zsol) <= ztol)
assert not info
def test_mcp_FB():
mcp = N.MCP_old(1, 1, mcp_function, mcp_Nablafunction)
z = np.array([0., 0.])
w = np.array([0., 0.])
SO = N.SolverOptions(mcp, N.SICONOS_MCP_OLD_FB)
N.mcp_old_driver_init(mcp, SO)
info = N.mcp_old_FischerBurmeister(mcp, z, w, SO)
N.mcp_old_driver_reset(mcp, SO)
print("z = ", z)
print("w = ", w)
assert (np.linalg.norm(z - zsol) <= ztol)
assert not info
def test_vi_1D():
vi = SN.VI(1, vi_function_1D)
vi.set_compute_nabla_F(vi_nabla_function_1D)
x = np.array([0.])
F = np.array([0.])
SO = SN.SolverOptions(vi, SN.SICONOS_VI_BOX_QI)
lb = np.array((-1.0, ))
ub = np.array((1.0, ))
vi.set_box_constraints(lb, ub)
info = SN.variationalInequality_box_newton_QiLSA(vi, x, F, SO)
print(info)
print("x = ", x)
print("F = ", F)
assert (np.linalg.norm(x - xsol_1D) <= xtol)
assert not info
def test_mcp_newton_min_FBLSA_2():
n = 10
mcp = sn.MCP(n - 5, 5, mcp_function_2, mcp_Nablafunction_2)
z = np.zeros(n)
w = np.zeros(n)
options = sn.SolverOptions(sn.SICONOS_MCP_NEWTON_MIN_FBLSA)
options.iparam[sn.SICONOS_IPARAM_STOPPING_CRITERION] = \
sn.SICONOS_STOPPING_CRITERION_RESIDU
sn.solver_options_print(options)
info = sn.mcp_newton_min_FBLSA(mcp, z, w, options)
print("z = ", z)
print("w = ", w)
#assert (np.linalg.norm(z-zsol) <= ztol)
assert not info
def test_mcp_newton_min_FBLSA_2():
mcp = SN.MCP(n - 5, 5, mcp_function_2, mcp_Nablafunction_2)
z = np.zeros(n)
w = np.zeros(n)
SO = SN.SolverOptions(mcp, SN.SICONOS_MCP_NEWTON_MIN_FBLSA)
SO.internalSolvers.iparam[
SN.
SICONOS_IPARAM_STOPPING_CRITERION] = SN.SICONOS_STOPPING_CRITERION_RESIDU
SN.solver_options_print(SO)
info = SN.mcp_newton_min_FBLSA(mcp, z, w, SO)
print("z = ", z)
print("w = ", w)
#assert (np.linalg.norm(z-zsol) <= ztol)
assert not info
def test_vi_2D():
vi = SN.VI(2, vi_function_2D)
vi.set_compute_nabla_F(vi_nabla_function_2D)
x = np.array((0., 0.))
F = np.array((0., 0.))
SO = SN.SolverOptions(vi, SN.SICONOS_VI_BOX_QI)
lb = np.array((-1.0, -1.0))
ub = np.array((1.0, 1.0))
vi.set_box_constraints(lb, ub)
info = SN.variationalInequality_box_newton_QiLSA(vi, x, F, SO)
print(info)
print('number of iteration {:} ; precision {:}'.format(SO.iparam[1], SO.dparam[1]))
print("x = ", x)
print("F = ", F)
assert (np.linalg.norm(x-xsol_2D) <= xtol)
assert not info
def test_vi_3D():
vi = sn.VI(3, vi_function_3D)
x = np.zeros((3, ))
F = np.zeros((3, ))
SO = sn.SolverOptions(sn.SICONOS_VI_BOX_QI)
vi.set_compute_nabla_F(vi_nabla_function_3D)
lb = np.array((-1.0, -1.0, -1.0))
ub = np.array((1.0, 1.0, 1.0))
vi.set_box_constraints(lb, ub)
info = sn.variationalInequality_box_newton_QiLSA(vi, x, F, SO)
print(info)
print('number of iteration {:} ; precision {:}'.format(
SO.iparam[sn.SICONOS_IPARAM_ITER_DONE],
SO.dparam[sn.SICONOS_DPARAM_RESIDU]))
print("x = ", x)
print("F = ", F)
assert (np.linalg.norm(x - xsol_3D) <= xtol)
assert not info
assert (np.abs(SO.dparam[sn.SICONOS_DPARAM_RESIDU]) < 1e-10)
def test_ncp_path():
ncp = SN.NCP(2, ncp_function, ncp_Nablafunction)
z = np.array([0., 0.])
w = np.array([0., 0.])
SO = SN.SolverOptions(SN.SICONOS_NCP_PATH)
info = SN.ncp_driver(ncp, z, w, SO)
if siconos.WITH_PATHFERRIS:
assert (np.linalg.norm(z - zsol) <= ztol)
assert not info
return
else:
assert info != 0
try:
SN.ncp_path(ncp, z, w, SO)
except RuntimeError:
pass
except:
assert 0
def __init__(self, name=None, gnuplot_name=None, API=None, TAG=None, iparam_iter=None,
dparam_err=None, maxiter=None, precision=None, with_guess=None):
self._name = name
if (gnuplot_name==None):
self._gnuplot_name = self._name
else:
self._gnuplot_name = gnuplot_name
self._API = API
self._TAG = TAG
self._iparam_iter = iparam_iter
self._dparam_err = dparam_err
self._SO = N.SolverOptions(TAG) # set default solver options
if (maxiter==None):
raise RuntimeError ("SiconosSolver() maxiter have to specified.")
else :
self._SO.iparam[0] = maxiter
if (precision==None):
raise RuntimeError ("SiconosSolver() precision have to specified.")
else:
self._SO.dparam[0] = precision
if (with_guess==None):
raise RuntimeError ("SiconosSolver() with_guess have to specified.")
else:
self._with_guess = with_guess
def test_lcp_lexicolemke():
SO = N.SolverOptions(lcp, N.SICONOS_LCP_LEMKE)
info = N.lcp_lexicolemke(lcp, z, w, SO)
print('lexicolemke iter =', SO.iparam[1])
assert (np.linalg.norm(z - zsol) <= ztol)
assert not info
def test_vi_C_interface():
try:
from cffi import FFI
cffi_is_present = True
except:
cffi_is_present = False
return
if cffi_is_present:
h = 1e-5
T = 1.0
t = 0.0
theta = 1.0
gamma = 1.0
g = 9.81
kappa = 0.4
xk = np.array((1., 10.))
ffi = FFI()
ffi.cdef('void set_cstruct(uintptr_t p_env, void* p_struct);')
ffi.cdef('''typedef struct
{
int id;
double* xk;
double h;
double theta;
double gamma;
double g;
double kappa;
unsigned int f_eval;
unsigned int nabla_eval;
} data;
''')
data_struct = ffi.new('data*')
data_struct.id = -1 # to avoid freeing the data in the destructor
data_struct.xk = ffi.cast('double *', xk.ctypes.data)
data_struct.h = h
data_struct.theta = theta
data_struct.gamma = gamma
data_struct.g = g
data_struct.kappa = kappa
vi = SN.VI(2)
import siconos
D = ffi.dlopen(siconos.__path__[0] + '/_numerics.so')
D.set_cstruct(vi.get_env_as_long(), ffi.cast('void*', data_struct))
vi.set_compute_F_and_nabla_F_as_C_functions('ZhuravlevIvanov.so',
'compute_F',
'compute_nabla_F')
lambda_ = np.zeros((2, ))
xkp1 = np.zeros((2, ))
SO = SN.SolverOptions(vi, SN.SICONOS_VI_BOX_QI)
lb = np.array((-1.0, -1.0))
ub = np.array((1.0, 1.0))
vi.set_box_constraints(lb, ub)
N = int(T / h + 10)
print(N)
SO.dparam[0] = 1e-24
SO.iparam[0] = 100
SO.iparam[2] = 1
SO.iparam[3] = 0
SO.iparam[4] = 5
signs = np.empty((N, 2))
sol = np.empty((N, 2))
sol[0, :] = xk
k = 0
#SN.numerics_set_verbose(3)
while t <= T:
k += 1
info = SN.variationalInequality_box_newton_QiLSA(
vi, lambda_, xkp1, SO)
#print('iter {:} ; solver iter = {:} ; prec = {:}'.format(k, SO.iparam[1], SO.dparam[1]))
if info > 0:
print(lambda_)
# vi_function(2, signs[k-1, :], xkp1)
lambda_[0] = -np.sign(xkp1[0])
lambda_[1] = -np.sign(xkp1[1])
if np.abs(xk[0]) < 1e-10:
lambda_[0] = 0.01
if np.abs(xk[1]) < 1e-10:
lambda_[1] = 0.01
print('ok lambda')
print(lambda_)
info = SN.variationalInequality_box_newton_QiLSA(
vi, lambda_, xkp1, SO)
print('iter {:} ; solver iter = {:} ; prec = {:}'.format(
k, SO.iparam[1], SO.dparam[1]))
if info > 0:
print('VI solver failed ! info = {:}'.format(info))
print(xk)
print(lambda_)
print(xkp1)
kaboom()
# else:
# print('iter {:} ; solver iter = {:} ; prec = {:}'.format(k, SO.iparam[1], SO.dparam[1]))
# vi_function(2, lambda_, xkp1)
sol[k, 0:2] = xkp1
np.copyto(xk, xkp1, casting='no')
signs[k, 0:2] = lambda_
t = k * h
data_struct.h = h
data_struct.theta = theta
data_struct.gamma = gamma
data_struct.g = g
data_struct.kappa = kappa
vi = SN.VI(2)
D = ffi.dlopen(SN._numerics.__file__)
D.set_cstruct(vi.get_env_as_long(), ffi.cast('void*', data_struct))
vi.set_compute_F_and_nabla_F_as_C_functions('ZhuravlevIvanovVI.so',
'compute_F', 'compute_nabla_F')
lambda_ = np.zeros((2, ))
xkp1 = np.zeros((2, ))
SO = SN.SolverOptions(vi, SN.SICONOS_VI_BOX_QI)
lb = np.array((-1.0, -1.0))
ub = np.array((1.0, 1.0))
vi.set_box_constraints(lb, ub)
N = int(T / h + 10)
print(N)
SO.dparam[0] = 1e-12
SO.iparam[0] = 50
SO.iparam[2] = 1
SO.iparam[3] = 0
SO.iparam[4] = 10
signs = np.empty((N, 2))
sol = np.empty((N, 2))
sol[0, :] = xk
xk = np.array((1., 10.))
T = 4.0
t = 0.0
z = np.zeros((4, ))
w = np.empty((4, ))
kappa = 0.41
g = 9.81
theta = 1.0
gamma = 1.0
zi_syst = ZI(h, xk, theta, gamma, kappa, g)
mcp = SN.MixedComplementarityProblem2(0, 4, zi_syst)
SO = SN.SolverOptions(mcp, SN.SICONOS_MCP_NEWTON_FBLSA)
SO.dparam[0] = 1e-24
SO.iparam[0] = 150
SO.iparam[3] = 2
SO.iparam[4] = 10
N = int(T / h + 10)
print(N)
lambdaPM = np.empty((N, 4))
signs = np.empty((N, 2))
sol = np.empty((N, 2))
sol[0, :] = xk
k = 0
while t <= T:
q[...] = [-1, 1, 3, -1, 1, 3, -1, 1, 3] # Friction coeff mu = [0.1, 0.1, 0.1] fc3d = sn.FrictionContactProblem(3, nc, W, q, mu) # Case 2 : use fclib-library, read hdf5 file # --- Set solver options --- # Check Friction_cst.h for a list of solvers ids. solver_options = sn.SolverOptions(sn.SICONOS_FRICTION_3D_NSGS)#sn.SICONOS_FRICTION_3D_FPP) eps = np.finfo(np.float64).eps solver_options.dparam[0] = 100 * eps # --- Set unknowns --- velocity = np.zeros(3 * nc, dtype=np.float64) reaction = np.zeros_like(velocity) # --- Call solver driver with predefined options --- sn.fc3d_driver(fc3d, reaction, velocity, solver_options)
def test_lcp_enum():
SO = N.SolverOptions(lcp, N.SICONOS_LCP_ENUM)
info = N.lcp_enum(lcp, z, w, SO)
assert (np.linalg.norm(z - zsol) <= ztol)
assert not info
def test_fc3dfischer():
"""Non-smooth Newton, Fischer-Burmeister.
"""
SO = sn.SolverOptions(sn.SICONOS_FRICTION_3D_NSN_FB)
solve(FCP, sn.fc3d_nonsmooth_Newton_FischerBurmeister, SO)
