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'])))
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
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'])))
'''
--------------------------------------------------
solver: gsubg problem: rjevsky1 (nVars: 10) type: NSP goal: minimum
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']
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
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 = {}
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'])))
'''
--------------------------------------------------
solver: gsubg problem: rjevsky1 (nVars: 10) type: NSP goal: minimum
iter objFunVal
0 1.573e+02
10 -7.780e-01
20 -8.410e-01
30 -8.410e-01
40 -8.410e-01
50 -8.412e-01
# 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
'''
lines = []
R = {}
for i, solver in enumerate(solvers):
p = NSP(
obj,
startPoint,
maxIter=1700,
name="Rzhevsky5 (nVars: " + str(n) + ")",
maxTime=300,
maxFunEvals=1e7,
color=Colors[i],
)
p.fTol = 0.5e-10
cb.TMP = 1000
cb.stat = {"dist": [], "f": [], "df": []}
p.fOpt = -34.408608965509742
p.maxTime = 30
r = p.solve(solver, iprint=1, xtol=1e-15, ftol=1e-15, gtol=1e-15, 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: rjevsky5 (nVars: 50) type: NSP goal: minimum
iter objFunVal
0 -2.241e+00
1 -3.056e+01
2 -3.387e+01
3 -3.427e+01
4 -3.434e+01
5 -3.436e+01
6 -3.439e+01
7 -3.439e+01
asa = lambda x: asarray(x).reshape(-1, 1)
lines = []
R = {}
for i, solver in enumerate(solvers):
p = NSP(obj,
startPoint,
maxIter=1700,
name='Rzhevsky5 (nVars: ' + str(n) + ')',
maxTime=300,
maxFunEvals=1e7,
color=Colors[i])
p.fTol = 0.5e-10
cb.TMP = 1000
cb.stat = {'dist': [], 'f': [], 'df': []}
p.fOpt = -34.408608965509742
p.maxTime = 30
r = p.solve(solver,
iprint=1,
xtol=1e-15,
ftol=1e-15,
gtol=1e-15,
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: rjevsky5 (nVars: 50) type: NSP goal: minimum
iter objFunVal
0 -2.241e+00
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))
