CVXPY error while running `test.py`
Closed this issue · 6 comments
RandyChen233 commented
Hi,
I tried running test.py by specifying a few parameters as the following:
import SET
import datetime
from run import *
from others import *
import numpy as np
import pickle
import copy
def data_capture(a, b, c):
data = {
'pos_list': copy.copy(a),
'position_list': copy.copy(b),
'terminal_index_list': copy.copy(c)
}
return data
def initialize():
agent_list=[]
for i in range(SET.Num):
agent_list+=[ uav(i,SET.ini_x[i],SET.ini_v[i],SET.target[i],SET.K) ]
return agent_list
def PLAN( Num, ini_x, ini_v,target,r_min,epsilon,h,K,episodes):
SET.initialize_set(Num, ini_x , ini_v ,target,r_min,epsilon ,h ,K,episodes)
obj = {}
ReachGoal=False
episodes=SET.episodes
agent_list=initialize()
collect_data(agent_list)
obj[0] = data_capture(SET.pos_list, SET.position_list, SET.terminal_index_list)
# the main loop
start =datetime.datetime.now()
end = start
for i in range(1,episodes+1):
end_last=end
obstacle_list=get_obstacle_list(agent_list,SET.Num)
# run one step
agent_list = run_one_step(agent_list,obstacle_list)
# print
end = datetime.datetime.now()
print("Step %s have finished, running time is %s"%(i,end-end_last))
collect_data(agent_list)
obj[i] = data_capture(SET.pos_list, SET.position_list, SET.terminal_index_list)
if ReachGoal:
break
ReachGoal=check_reach_target(agent_list)
obj['goal'] = SET.target
return obj,agent_list
if __name__ == "__main__":
Num = 3
ini_x = [[0 ,0, 0],[2.0, 2.0, 2.0],[-1.5,1.6,1.8]]
ini_v = [[0.01, 0, 0.01], [0, 0, 0], [0, 0, 0]]
target = [[1.2, 1.3, 0.6],[ -0.4, -0.6, 1.1], [1.5, -1.5, 0.55]]
K=15
h = 0.1
episodes = 20
r_min = 0.3
epsilon= 0.1
obj, agents_list = PLAN(Num, ini_x, ini_v,target,r_min,epsilon,h,K,episodes)
print(f'Obj is :{obj}\n')
print(f' Agent_list is {agents_list}')
However, the following error occurred:
Traceback (most recent call last):
File "/home/randychen233/.local/lib/python3.10/site-packages/cvxpy/utilities/linalg.py", line 113, in is_psd_within_tol
ev = SA_eigsh(-tol) # might return np.NaN, or raise exception
File "/home/randychen233/.local/lib/python3.10/site-packages/cvxpy/utilities/linalg.py", line 97, in SA_eigsh
return sparla.eigsh(A, k=1, sigma=sigma, which='SA', v0=v0,
File "/home/randychen233/.local/lib/python3.10/site-packages/scipy/sparse/linalg/_eigen/arpack/arpack.py", line 1697, in eigsh
params.iterate()
File "/home/randychen233/.local/lib/python3.10/site-packages/scipy/sparse/linalg/_eigen/arpack/arpack.py", line 571, in iterate
self._raise_no_convergence()
File "/home/randychen233/.local/lib/python3.10/site-packages/scipy/sparse/linalg/_eigen/arpack/arpack.py", line 377, in _raise_no_convergence
raise ArpackNoConvergence(msg % (num_iter, k_ok, self.k), ev, vec)
scipy.sparse.linalg._eigen.arpack.arpack.ArpackNoConvergence: ARPACK error -1: No convergence (451 iterations, 0/1 eigenvectors converged)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/randychen233/projects/ICON_lab/IMPC-DR/3D/test.py", line 88, in <module>
obj, agents_list = PLAN(Num, ini_x, ini_v,target,r_min,epsilon,h,K,episodes)
File "/home/randychen233/projects/ICON_lab/IMPC-DR/3D/test.py", line 53, in PLAN
agent_list = run_one_step(agent_list,obstacle_list)
File "/home/randychen233/projects/ICON_lab/IMPC-DR/3D/run.py", line 50, in run_one_step
agent_list=[run_one_agent(items[i]) for i in range(SET.Num)]
File "/home/randychen233/projects/ICON_lab/IMPC-DR/3D/run.py", line 50, in <listcomp>
agent_list=[run_one_agent(items[i]) for i in range(SET.Num)]
File "/home/randychen233/projects/ICON_lab/IMPC-DR/3D/run.py", line 69, in run_one_agent
agent.cache=run_cvxp(agent)
File "/home/randychen233/projects/ICON_lab/IMPC-DR/3D/run.py", line 142, in run_cvxp
prob.solve()
File "/home/randychen233/.local/lib/python3.10/site-packages/cvxpy/problems/problem.py", line 503, in solve
return solve_func(self, *args, **kwargs)
File "/home/randychen233/.local/lib/python3.10/site-packages/cvxpy/problems/problem.py", line 1072, in _solve
data, solving_chain, inverse_data = self.get_problem_data(
File "/home/randychen233/.local/lib/python3.10/site-packages/cvxpy/problems/problem.py", line 646, in get_problem_data
solving_chain = self._construct_chain(
File "/home/randychen233/.local/lib/python3.10/site-packages/cvxpy/problems/problem.py", line 898, in _construct_chain
return construct_solving_chain(self, candidate_solvers, gp=gp,
File "/home/randychen233/.local/lib/python3.10/site-packages/cvxpy/reductions/solvers/solving_chain.py", line 217, in construct_solving_chain
reductions = _reductions_for_problem_class(problem, candidates, gp, solver_opts)
File "/home/randychen233/.local/lib/python3.10/site-packages/cvxpy/reductions/solvers/solving_chain.py", line 124, in _reductions_for_problem_class
if not gp and not problem.is_dcp():
File "/home/randychen233/.local/lib/python3.10/site-packages/cvxpy/utilities/performance_utils.py", line 73, in _compute_once
result = func(self, *args, **kwargs)
File "/home/randychen233/.local/lib/python3.10/site-packages/cvxpy/problems/problem.py", line 257, in is_dcp
return all(
File "/home/randychen233/.local/lib/python3.10/site-packages/cvxpy/problems/problem.py", line 258, in <genexpr>
expr.is_dcp(dpp) for expr in self.constraints + [self.objective])
File "/home/randychen233/.local/lib/python3.10/site-packages/cvxpy/problems/objective.py", line 153, in is_dcp
return self.args[0].is_convex()
File "/home/randychen233/.local/lib/python3.10/site-packages/cvxpy/utilities/performance_utils.py", line 73, in _compute_once
result = func(self, *args, **kwargs)
File "/home/randychen233/.local/lib/python3.10/site-packages/cvxpy/atoms/atom.py", line 178, in is_convex
if not (arg.is_affine() or
File "/home/randychen233/.local/lib/python3.10/site-packages/cvxpy/utilities/performance_utils.py", line 73, in _compute_once
result = func(self, *args, **kwargs)
File "/home/randychen233/.local/lib/python3.10/site-packages/cvxpy/expressions/expression.py", line 221, in is_affine
return self.is_constant() or (self.is_convex() and self.is_concave())
File "/home/randychen233/.local/lib/python3.10/site-packages/cvxpy/utilities/performance_utils.py", line 73, in _compute_once
result = func(self, *args, **kwargs)
File "/home/randychen233/.local/lib/python3.10/site-packages/cvxpy/atoms/atom.py", line 176, in is_convex
elif self.is_atom_convex():
File "/home/randychen233/.local/lib/python3.10/site-packages/cvxpy/atoms/quad_form.py", line 69, in is_atom_convex
return P.is_constant() and P.is_psd()
File "/home/randychen233/.local/lib/python3.10/site-packages/cvxpy/utilities/performance_utils.py", line 73, in _compute_once
result = func(self, *args, **kwargs)
File "/home/randychen233/.local/lib/python3.10/site-packages/cvxpy/expressions/constants/constant.py", line 227, in is_psd
self._psd_test = eig_util.is_psd_within_tol(self.value, s.EIGVAL_TOL)
File "/home/randychen233/.local/lib/python3.10/site-packages/cvxpy/utilities/linalg.py", line 129, in is_psd_within_tol
raise sparla.ArpackNoConvergence(error_with_note, e.eigenvalues, e.eigenvectors)
scipy.sparse.linalg._eigen.arpack.arpack.ArpackNoConvergence: ARPACK error -1: ARPACK error -1: No convergence (451 iterations, 0/1 eigenvectors converged)
CVXPY note: This failure was encountered while trying to certify
that a matrix is positive semi-definite (see [1] for a definition).
In rare cases, this method fails for numerical reasons even when the matrix is
positive semi-definite. If you know that you're in that situation, you can
replace the matrix A by cvxpy.psd_wrap(A).
[1] https://en.wikipedia.org/wiki/Definite_matrix
I wonder what would be causing CVXPY to fail here?
CYDXYYJ commented
@RandyChen233 Have you successfully run CVXPY in WSL before make any modification?
RandyChen233 commented
@CYDXYYJ Hi,
Yes, I have been using CVXPY for a while in WSL, and I have not made any modifications.
CYDXYYJ commented
@RandyChen233 Ok, I'll test your code in this weekend and give you a response in the next week.
RandyChen233 commented
Thank you ! @CYDXYYJ I also have a question about the distributed nature of the algorithm, and I wonder if you could clarify it: should each agent i only receives position information from its one-hop neighbor j to compute its buffered Voronoi cell? or should agent i considers all other agents j that is in the configuration space?
CYDXYYJ commented
@RandyChen233 Agent i only need to communicate with the otehrs in a finite rage. This property can be found in paper which can be found in arxiv. If you have any other questions, you can contact with me via e-mail which can be found in my Github profile.
CYDXYYJ commented
@RandyChen233 I have tested your code, you'd better change it as
ini_x = np.array([[0 ,0, 0],[2.0, 2.0, 2.0],[-1.5,1.6,1.8]])
ini_v = np.array([[0.01, 0, 0.01], [0, 0, 0], [0, 0, 0]])
target = np.array([[1.2, 1.3, 0.6],[ -0.4, -0.6, 1.1], [1.5, -1.5, 0.55]])