def solve(self, stepsize=1.0 / scalar_var('iter'), cur=None):
if isNumber(stepsize): stepsize = scalar(stepsize)
if self.type == MAXIMIZE:
self.obj = -self.obj
vars = self.obj.get_vars()
if cur == None:
cur = {}
for var in vars:
cur[var] = 0.0
optval = None
for iter in range(1, MAXITERS + 1):
f = self.obj.get_value(cur)
g = None
maxres = 0
for cons in self.constraints:
if cons.get_value(cur) > maxres:
maxres = cons.get_value(cur)
g = cons.subgrad(cur)
if g == None: g = self.obj.subgrad(cur)
norm = math.sqrt(sum([x**2 for x in g.itervalues()]))
if norm < EPS: break
nxt = {}
if self.type == MAXIMIZE:
stepsizedic = {'f': -f, 'gnorm': norm, 'iter': iter}
else:
stepsizedic = {'f': f, 'gnorm': norm, 'iter': iter}
alpha = stepsize.get_value(stepsizedic)
assert alpha > 0
for (key, val) in g.iteritems():
nxt[key] = cur[key] - val * alpha
cur = nxt
if self.type == MAXIMIZE:
self.obj = -self.obj
optval = self.obj.get_value(cur)
return (optval, cur)
def solve(self, stepsize = 1.0/scalar_var('iter'), cur = None):
if isNumber(stepsize): stepsize = scalar(stepsize)
if self.type == MAXIMIZE:
self.obj = -self.obj
vars = self.obj.get_vars()
if cur == None:
cur = {}
for var in vars: cur[var] = 0.0
optval = None
for iter in range(1, MAXITERS+1):
f = self.obj.get_value(cur)
g = None
maxres = 0
for cons in self.constraints:
if cons.get_value(cur) > maxres:
maxres = cons.get_value(cur)
g = cons.subgrad(cur)
if g == None: g = self.obj.subgrad(cur)
norm = math.sqrt(sum([x**2 for x in g.itervalues()]))
if norm < EPS: break
nxt = {}
if self.type == MAXIMIZE:
stepsizedic = {'f': -f, 'gnorm': norm, 'iter': iter}
else:
stepsizedic = {'f': f, 'gnorm': norm, 'iter': iter}
alpha = stepsize.get_value(stepsizedic)
assert alpha > 0
for (key, val) in g.iteritems():
nxt[key] = cur[key]-val*alpha
cur = nxt
if self.type == MAXIMIZE:
self.obj = -self.obj
optval = self.obj.get_value(cur)
return (optval, cur)
def __init__(self, type, obj, constraints):
if isNumber(obj): obj = scalar(obj)
assert type in [MINIMIZE, MAXIMIZE]
assert (type == MINIMIZE and obj.is_convex()) or (type == MAXIMIZE and obj.is_concave())
self.type = type
self.obj = obj
self.constraints = []
for cons in constraints:
if cons.relop == LT or cons.relop == EQ:
self.constraints.append(cons.lhs-cons.rhs)
if cons.relop == GT or cons.relop == EQ:
self.constraints.append(cons.rhs-cons.lhs)
def __init__(self, type, obj, constraints):
if isNumber(obj): obj = scalar(obj)
assert type in [MINIMIZE, MAXIMIZE]
assert (type == MINIMIZE and obj.is_convex()) or (type == MAXIMIZE
and obj.is_concave())
self.type = type
self.obj = obj
self.constraints = []
for cons in constraints:
if cons.relop == LT or cons.relop == EQ:
self.constraints.append(cons.lhs - cons.rhs)
if cons.relop == GT or cons.relop == EQ:
self.constraints.append(cons.rhs - cons.lhs)
