def testSpecificProblemDimTwo(self):
"""
A specific problem of dimension two has been choosen to be tested.
"""
# prepare set of testing cases
f = {(0, 0): 5, (1, 0): -2, (2, 0): 1, (0, 1): -4, (0, 2): 1}
g = {(0, 0): 3**2, (0, 2): -1, (2, 0): -1}
solution = matrix([[1], [2]])
# test all cases
for degree in range(1, 3):
with self.subTest(i = degree):
# init prolem
problem = POPSolver(f, g, degree)
# solve and compare the results
x = problem.solve(problem.getFeasiblePoint(3))
self.assertLessEqual(norm(x - solution), 10**(-3))
from numpy import *
from POPSolver import POPSolver
# objective function
# f(x, y) = (x - 1)^2 + (y - 2)^2
# = x^2 -2*x + y^2 - 4*y + 5
# global minima at (1, 2)
f = {(0, 0): 5, (1, 0): -2, (2, 0): 1, (0, 1): -4, (0, 2): 1}
# constraint function
# g(x, y) = 9 - x^2 - y^2
g = {(0, 0): 3**2, (2, 0): -1, (0, 2): -1}
# degree of the relaxation
d = 2
# initialize the solver
POP = POPSolver(f, g, d)
# obtain some feasible point for the SDP problem (within ball with radius 3)
y0 = POP.getFeasiblePoint(3)
# enable outputs
POP.setPrintOutput(True)
#solve the problem
x = POP.solve(y0)
print(x)
minD = int(ceil(dF/2))
# for relaxation order
for dR in order:
d = dR + minD
# clear measured time
t = 0
# for different feasible points
for i in N:
# startup time
timeStart = timeit.default_timer()
# initialize the solver
problem = POPSolver(f, g, d)
# obtain some feasible point for the SDP problem
y0 = problem.getFeasiblePoint(1)
#solve the problem
problem.solve(y0)
# final time
t += timeit.default_timer() - timeStart
print((n, dF, dR))
results[n, dF, dR] = t/len(N)
save('performanceResults/performanceResults.npy', results)
print(results)
