def main():
x = Variable(1)
y = Variable(1)
constraints = [x >= 0, y >= 0, x+y == 1]
objective = Maximize(square(x) + square(y))
problem = Problem(objective, constraints)
print("problem is DCP:", problem.is_dcp())
print("problem is DCCP:", is_dccp(problem))
problem.solve(method='dccp')
print('solution (x,y): ', x.value, y.value)
def describe_problem(problem: Problem):
"""
DMCP https://github.com/cvxgrp/dmcp
dccp
"""
st = 'Curvature {}'.format(problem.objective.expr.curvature)
st += '\nis disciplined quasiconvex {}'.format(problem.is_dqcp())
st += '\nis disciplined geometric {}'.format(problem.is_dgp())
st += '\nis disciplined quadratic {}'.format(problem.is_qp())
st += '\nis disciplined convex {}'.format(problem.is_dcp())
st += '\nis disciplined concave-convex {}'.format(dccp.is_dccp(problem))
st += '\nis disciplined multi-convex {}'.format(dmcp.is_dmcp(problem))
# todo
# SQP sequential quadratic program
# SCP seperable convex program
#
return st
# prob = Problem(
# Minimize(
# cvx.max(
# cvx.max(cvx.abs(c), axis=1) + r # max dim = c_max + r
# )
# ),
# constr
# )
prob = Problem(
Minimize(
cvx.max(cvx.abs(c[:, 0]) + r) + cvx.max(cvx.abs(c[:, 1]) + r)),
constr)
print(prob.is_dcp(), prob.is_dcp())
print(dccp.is_dccp(prob))
prob.solve(method='dccp',
solver='ECOS',
ep=1e-2,
max_slack=1e-2,
verbose=True)
l = cvx.max(cvx.max(cvx.abs(c), axis=1) + r).value * 2
pi = np.pi
ratio = pi * cvx.sum(cvx.square(r)).value / cvx.square(l).value
print("ratio =", ratio)
# plot
plt.figure(figsize=(5, 5))
circ = np.linspace(0, 2 * pi)
