def maximize_fapx_cvxopt( node, problem, scoop=False):
'''
Finds the value of y solving maximize sum(fapx(x)) subject to:
constr
l <= x <= u
fapx is calculated by approximating the functions given in fs
as the concave envelope of the function that is tight,
for each coordinate i, at the points in tight[i]
fs should be a list of tuples containing the function and its derivative
corresponding to each coordinate of the vector x.
Returns a dict containing the optimal variable y as a list,
the optimal value of the approximate objective,
and the value of sum(f(x)) at x.
scoop = True optionally dumps all problem parameters into a file
which can be parsed and solved using the scoop second order cone
modeling language
'''
n = len(node.l);
l = matrix(node.l); u = matrix(node.u)
x = problem.variable
constr = problem.constr
# add box constraints
box = [x[i]<=u[i] for i in xrange(n)] + [x[i]>=l[i] for i in xrange(n)]
# find approximation to concave envelope of each function
(fapx,slopes,offsets,fapxs) = utilities.get_fapx(node.tight,problem.fs,l,u,y=x)
if problem.check_z:
utilities.check_z(problem,node,fapx,x)
if scoop:
utilities.scoop(p,node,slopes,offsets)
obj = sum( fapx )
o = op(obj,constr + box)
try:
o.solve(solver = 'glpk')
except:
o.solve()
if not o.status == 'optimal':
if o.status == 'unknown':
raise ImportError('Unable to solve subproblem. Please try again after installing cvxopt with glpk binding.')
else:
# This node is dead, since the problem is infeasible
return False
else:
# find the difference between the fapx and f for each coordinate i
fi = numpy.array([problem.fs[i][0](x.value[i]) for i in range(n)])
fapxi = numpy.array([list(-fun.value())[0] for fun in fapx])
#if verbose: print 'fi',fi,'fapxi',fapxi
maxdiff_index = numpy.argmax( fapxi - fi )
results = {'x': list(x.value), 'fapx': -list(obj.value())[0], 'f': float(sum(fi)), 'maxdiff_index': maxdiff_index}
return results
def maximize_fapx_glpk( node, problem, verbose = False ):
'''
Finds the value of y solving maximize sum(fapx(x)) subject to:
constr
l <= x <= u
fapx is calculated by approximating the functions given in fs
as the concave envelope of the function that is tight,
for each coordinate i, at the points in tight[i]
fs should be a list of tuples containing the function and its derivative
corresponding to each coordinate of the vector x.
Returns a dict containing the optimal variable y as a list,
the optimal value of the approximate objective,
and the value of sum(f(x)) at x.
'''
n = len(node.l);
l = node.l; u = node.u
# find approximation to concave envelope of each function
(slopes,offsets,fapxs) = utilities.get_fapx(node.tight,problem.fs,l,u)
# verify correctness of concave envelope
if problem.check_z:
utilities.check_z(problem,node,fapxs,x)
# formulate concave problem as lp and solve
lp = sigopt2pyglpk(slopes = slopes,offsets=offsets,l=l,u=u,**problem.constr)
lp.simplex()
# find the difference between the fapx and f for each coordinate i
xstar = [c.primal for c in lp.cols[:n]]
fi = numpy.array([problem.fs[i][0](xstar[i]) for i in range(n)])
fapxi = numpy.array([c.primal for c in lp.cols[n:]])
maxdiff_index = numpy.argmax( fapxi - fi )
results = {'x': xstar, 'fapx': lp.obj.value, 'f': float(sum(fi)), 'maxdiff_index': maxdiff_index}
if verbose: print 'fi',fi,'fapxi',fapxi,results
return results
