return False
asa = lambda x: asarray(x).reshape(-1, 1)
Colors = ['r', 'k', 'b']
R = {}
for i, solver in enumerate(solvers):
p = NSP(obj,
startPoint,
maxIter=1700,
name='Rzhevsky4 (nVars: ' + str(n) + ')',
maxTime=300,
maxFunEvals=1e7,
color=Colors[i])
p.fTol = 0.5e-20
p.fOpt = 0.0
cb.TMP = 1000
cb.stat = {'dist': [], 'f': [], 'df': []}
r = p.solve(solver,
iprint=10,
xtol=1e-20,
ftol=1e-20,
debug=0,
show=solver == solvers[-1],
plot=0,
callback=cb)
R[solver] = hstack(
(asa(cb.stat['dist']), asa(cb.stat['f']), asa(cb.stat['df'])))
'''
--------------------------------------------------
cb.stat['dist'].append(tmp)
cb.stat['f'].append(p.nEvals['f'])
cb.stat['df'].append(-p.nEvals['df'])
return False
asa = lambda x:asarray(x).reshape(-1, 1)
Colors = ['r', 'k','b']
lines = []
R = {}
for i, solver in enumerate(solvers):
p = NSP(obj, startPoint, fixedVars=(x[0], x[-1]), maxTime = 20, name = 'Rzhevsky7 (nVars: ' + str(n)+')', maxFunEvals=1e7, color = Colors[i])
p._prepare()
p.c=None
#p.fEnough = 2.08983385058799+4e-10
p.fOpt = obj(T_optPoint)
p.fTol = 0.5e-15
cb.TMP = 1000
cb.stat = {'dist':[], 'f':[], 'df':[]}
r = p.solve(solver, iprint=10, xtol = 1e-10, ftol = 1e-16, gtol = 1e-10, debug=0, show = solver == solvers[-1], plot = 0, callback = cb)
print('objective evals: %d gradient evals: %d ' % (r.evals['f'],r.evals['df']))
print('distance to f*: %0.1e' % (r.ff-p.fOpt))
print('distance to x*: %0.1e' % (norm(asarray(X) - hstack((X[0], p.xk, X[-1])))))
R[solver] = hstack((asa(cb.stat['dist']), asa(cb.stat['f']), asa(cb.stat['df'])))
'''
--------------------------------------------------
solver: gsubg problem: rjevsky6 (nVars: 30) type: NSP goal: minimum
iter objFunVal log10(MaxResidual/ConTol)
0 2.954e+01 -100.00
OpenOpt Warning: Handling of constraints is not implemented properly for the solver gsubg yet
10 4.002e+00 -100.00
20 2.232e+00 -100.00
solvers = ['ralg', 'amsg2p']
solvers = ['gsubg']
Colors = ['r', 'k','b']
xOpt = 1.0/n
def cb(p):
tmp = ceil(log10(norm(xOpt - p.xk)))
if tmp < cb.TMP:
# print 'distance:', tmp, 'itn:', p.iter, 'n_func:', p.nEvals['f'], 'n_grad:', -p.nEvals['df']
cb.TMP = tmp
cb.stat['dist'].append(tmp)
cb.stat['f'].append(p.nEvals['f'])
cb.stat['df'].append(-p.nEvals['df'])
return False
asa = lambda x:asarray(x).reshape(-1, 1)
R = {}
lines = []
for i, solver in enumerate(solvers):
p = NSP(obj, startPoint, maxIter = 4700, name = 'Rzhevsky3 (nVars: ' + str(n)+')', maxTime = 30000, maxFunEvals=1e7, color = Colors[i])
#p.maxIter = 10#; p.useSparse = False
p.fEnough = 1.0e-5
p.fOpt = 1.0e-5
p.fTol = 0.5e-5
cb.TMP = 1000
cb.stat = {'dist':[], 'f':[], 'df':[]}
r = p.manage(solver, iprint=1, xtol = 1e-10, ftol = 1e-10, show = solver == solvers[-1], plot = 0, callback = cb)
R[solver] = hstack((asa(cb.stat['dist']), asa(cb.stat['f']), asa(cb.stat['df'])))
def __solver__(self, p):
if SMALL_DELTA_X in p.kernelIterFuncs.keys(): p.kernelIterFuncs.pop(SMALL_DELTA_X)
if SMALL_DELTA_F in p.kernelIterFuncs.keys(): p.kernelIterFuncs.pop(SMALL_DELTA_F)
if self.nspSolver == 'autoselect':
nspSolver = 'amsg2p' if p.isUC else 'ralg'
else:
nspSolver = self.nspSolver
# nspSolver = 'ralg'
way = 3 if nspSolver == 'ralg' else 2
if way == 1:
use2 = False
f = lambda x: sum(abs(p.f(x)))
def df(x):
return dot(p.df(x), sign(p.f(x)))
elif way == 2:
use2 = True
f = lambda x: sum(p.f(x)**2)
def df(x):
return 2.0*dot(p.f(x), p.df(x))
elif way == 3:
use2 = False
f = lambda x: max(abs(p.f(x)))
def df(x):
F = p.f(x)
ind = argmax(abs(F))
return p.df(x, ind) * sign(F[ind])
FTOL = p.ftol**2 if use2 else p.ftol
def iterfcn(*args, **kwargs):
p2.primalIterFcn(*args, **kwargs)
p.xk = p2.xk.copy()
Fk = norm(p.f(p.xk), inf)
p.rk = p.getMaxResidual(p.xk)
#TODO: ADD p.rk
if p.nEvals['f'] > p.maxFunEvals:
p.istop = p2.istop = IS_MAX_FUN_EVALS_REACHED
elif p2.istop!=0:
if Fk < FTOL and p.rk < p.contol:
p.istop = 15
msg_contol = '' if p.isUC else 'and contol '
p.msg = 'solution with required ftol ' + msg_contol+ 'has been reached'
else: p.istop = p2.istop
p.iterfcn()
return p.istop
p2 = NSP(f, p.x0, df=df, xtol = p.xtol/1e16, gtol = p.gtol/1e16,\
A=p.A, b=p.b, Aeq=p.Aeq, beq=p.beq, lb=p.lb, ub=p.ub, \
maxFunEvals = p.maxFunEvals, fEnough = FTOL, maxIter=p.maxIter, iprint = -1, \
maxtime = p.maxTime, maxCPUTime = p.maxCPUTime, noise = p.noise, fOpt = 0.0)
if p.userProvided.c:
p2.c, p2.dc = p.c, p.dc
if p.userProvided.h:
p2.h, p2.dh = p.h, p.dh
p2.primalIterFcn, p2.iterfcn = p2.iterfcn, iterfcn
if p.debug: p2.iprint = 1
if nspSolver == 'ralg':
if p.isUC: p2.ftol = p.ftol / 1e16
else: p2.ftol = 0.0
else:
p2.ftol = 0.0
p2.xtol = 0.0
p2.gtol = 0.0
if use2:
p2.fTol = 0.5*p.ftol ** 2
else:
p2.fTol = 0.5*p.ftol
r2 = p2.solve(nspSolver)
#xf = fsolve(p.f, p.x0, fprime=p.df, xtol = p.xtol, maxfev = p.maxFunEvals)
xf = r2.xf
p.xk = p.xf = xf
p.fk = p.ff = asfarray(norm(p.f(xf), inf)).flatten()
cb.stat['f'].append(p.nEvals['f'])
cb.stat['df'].append(-p.nEvals['df'])
return False
asa = lambda x: asarray(x).reshape(-1, 1)
R = {}
for i, solver in enumerate(solvers):
p = NSP(obj,
startPoint,
maxIter=17000,
name='Rzhevsky1 (nVars: ' + str(n) + ')',
maxTime=300,
maxFunEvals=1e7,
color=Colors[i])
p.fTol = 0.5e-10
p.fEnough = -0.84140833459
p.fOpt = -0.841408334596
cb.TMP = 1000
cb.stat = {'dist': [], 'f': [], 'df': []}
r = p.solve(solver,
iprint=10,
ftol=1e-15,
xtol=1e-10,
debug=0,
show=solver == solvers[-1],
plot=0,
callback=cb)
R[solver] = hstack(
(asa(cb.stat['dist']), asa(cb.stat['f']), asa(cb.stat['df'])))
'''
solvers = [oosolver('amsg2p', gamma=2.0)]
Colors = ['r', 'k', 'b']
lines = []
R = {}
for Tol_p in range(-10, -31, -1):
#print('Tol = 10^%d' % Tol_p)
for i, solver in enumerate(solvers):
p = NSP(obj,
startPoint,
maxIter=1700,
name='Rzhevsky6 (nVars: ' + str(n) + ')',
maxTime=300,
maxFunEvals=1e7,
color=Colors[i])
p.fOpt = 0.0
p.fTol = 10**Tol_p
cb.TMP = 1000
cb.stat = {'dist': [], 'f': [], 'df': []}
r = p.solve(solver, iprint=-1, xtol=0, ftol=0, callback=cb)
R[solver] = hstack(
(asa(cb.stat['dist']), asa(cb.stat['f']), asa(cb.stat['df'])))
print('itn df dx %d %0.1e %0.1e' %
(-r.evals['df'], r.ff - p.fOpt, norm(p.xk - 1.0)))
# print('objective evals: %d gradient evals: %d ' % (r.evals['f'],r.evals['df']))
# print('distance to f*: %0.1e' % (r.ff-p.fOpt))
# print('distance to x*: %0.1e' % norm(p.xk-1.0))
'''
--------------------------------------------------
solver: gsubg problem: rjevsky6 (nVars: 15) type: NSP goal: minimum
iter objFunVal
0 2.408e+02
from numpy import arange
from numpy.linalg import norm
from openopt import NSP, oosolver
from FuncDesigner import *
N = 300
x = oovar('x')
startPoint = {x: 1 + 1.0 / arange(1, N)}
S = 1e4 ** (1.0/arange(1, N))
#f = abs(x[0]) + S * abs(x[1]) + S**2 * abs(x[2])
f = sum(abs(x)*S)
solvers = [oosolver('ralg')]
solvers = [oosolver('gsubg', addASG = True)]
#solvers = [oosolver('gsubg', zhurb = 20, dual=False)]
#solvers = ['ipopt']
#solvers = ['slmvm2']
#solvers = ['mma']
for solver in solvers:
p = NSP(f, startPoint, maxIter = 10000, maxTime = 15000, maxFunEvals=1e7)
p.fEnough = 1.5e1
p.fTol = 1.0e1
#p.constraints = (y > 5)(tol=1e-4) #x>1e-1 #[2*y<sin(arange(N))]
#r = p.solve(solver, iprint=10, xtol = 1e-36, ftol = 1e-16, show = solver == solvers[-1])
r = p.solve(solver, iprint=10, xtol = 1e-16, ftol = 1e-6, show = solver == solvers[-1])
if tmp < cb.TMP:
# print 'distance:', tmp, 'itn:', p.iter, 'n_func:', p.nEvals['f'], 'n_grad:', -p.nEvals['df']
cb.TMP = tmp
cb.stat['dist'].append(tmp)
cb.stat['f'].append(p.nEvals['f'])
cb.stat['df'].append(-p.nEvals['df'])
return False
asa = lambda x:asarray(x).reshape(-1, 1)
Colors = ['r', 'k','b']
R = {}
for i, solver in enumerate(solvers):
p = NSP(obj, startPoint, maxIter = 1700, name = 'Rzhevsky4 (nVars: ' + str(n)+')', maxTime = 300, maxFunEvals=1e7, color = Colors[i])
p.fTol = 0.5e-20
p.fOpt = 0.0
cb.TMP = 1000
cb.stat = {'dist':[], 'f':[], 'df':[]}
r = p.solve(solver, iprint=10, xtol = 1e-20, ftol = 1e-20, debug=0, show = solver == solvers[-1], plot = 0, callback = cb)
R[solver] = hstack((asa(cb.stat['dist']), asa(cb.stat['f']), asa(cb.stat['df'])))
'''
--------------------------------------------------
solver: gsubg problem: rjevsky3 (nVars: 10) type: NSP goal: minimum
iter objFunVal
0 9.789e-01
10 5.155e-03
12 2.168e-03
istop: 16 (optimal solution wrt required fTol has been obtained)
Solver: Time Elapsed = 1.1 CPU Time Elapsed = 1.1
objFunValue: 0.002167903
cb.stat['dist'].append(tmp)
cb.stat['f'].append(p.nEvals['f'])
cb.stat['df'].append(-p.nEvals['df'])
return False
asa = lambda x:asarray(x).reshape(-1, 1)
solvers = ['ralg', 'amsg2p', 'gsubg']
solvers = [oosolver('amsg2p', gamma = 2.0)]
Colors = ['r', 'k','b']
lines = []
R = {}
for Tol_p in range(-10, -31, -1):
#print('Tol = 10^%d' % Tol_p)
for i, solver in enumerate(solvers):
p = NSP(obj, startPoint, maxIter = 1700, name = 'Rzhevsky6 (nVars: ' + str(n)+')', maxTime = 300, maxFunEvals=1e7, color = Colors[i])
p.fOpt = 0.0
p.fTol = 10**Tol_p
cb.TMP = 1000
cb.stat = {'dist':[], 'f':[], 'df':[]}
r = p.solve(solver, iprint=-1, xtol = 0, ftol = 0, callback = cb)
R[solver] = hstack((asa(cb.stat['dist']), asa(cb.stat['f']), asa(cb.stat['df'])))
print('itn df dx %d %0.1e %0.1e' % (-r.evals['df'], r.ff-p.fOpt, norm(p.xk-1.0)))
# print('objective evals: %d gradient evals: %d ' % (r.evals['f'],r.evals['df']))
# print('distance to f*: %0.1e' % (r.ff-p.fOpt))
# print('distance to x*: %0.1e' % norm(p.xk-1.0))
'''
--------------------------------------------------
solver: gsubg problem: rjevsky6 (nVars: 15) type: NSP goal: minimum
iter objFunVal
0 2.408e+02
1 9.712e+01
2 5.315e+01
from numpy import arange
from numpy.linalg import norm
from openopt import NSP, oosolver
from FuncDesigner import *
N = 100000
x = oovar('x')
startPoint = {x: 1 + 1.0 / arange(1, N+1)}
S = 1e4 ** (1.0/arange(1, N+1))
arr = sin(arange(N))
f = sum((x-arr)**2 * S) / 1e4
solvers = [oosolver('ralg')]
solvers = [oosolver('gsubg', dual=True, zhurb = 50)]
#solvers = [oosolver('gsubg', zhurb = 20, dual=False)]
#solvers = ['ipopt']
#solvers = ['slmvm2']
#solvers = ['mma']
for solver in solvers:
p = NSP(f, startPoint, maxIter = 10000, maxTime = 15000, maxFunEvals=1e7)
p.fEnough = 1.5e-1
p.fTol = 5.0e-1
#p.constraints = (y > 5)(tol=1e-4) #x>1e-1 #[2*y<sin(arange(N))]
#r = p.solve(solver, iprint=10, xtol = 1e-36, ftol = 1e-16, show = solver == solvers[-1])
r = p.manage(solver, iprint=1, xtol = 1e-8, ftol = 1e-7, show = solver == solvers[-1])
