import csv
import numpy as np
from numpy import sin, cos, tan, pi, exp, sqrt
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.axes3d import Axes3D
from matplotlib import cm
import time
import sys
# optimization parameters
x0 = [0.0, 0.0]
#algorism = 'sd'
algorism = 'cg'
# for line search '' or 'one', 'simple', 'exact', 'golden', 'armijo'
lsmode = 'simple'
nmaxlsiter = 100
# for lsmode == 'golden'
alphaeps = 1.0e-2
xrange = 0.5
# for lsmode == 'exact'
dump = 0.3
h = 1.0e-3
# for global optimization
eps = 1.0e-5
nmaxiter = 200
iprintiterval = 1
# target function
#functype = 'ellipsoid'
functype = 'general'
if __name__ == "__main__":
if len(sys.argv) >= 2:
x0[0] = float(sys.argv[1])
if len(sys.argv) >= 3:
x0[1] = float(sys.argv[2])
if len(sys.argv) >= 4:
algorism = sys.argv[3]
if len(sys.argv) >= 5:
lsmode = sys.argv[4]
if len(sys.argv) >= 6:
functype = sys.argv[5]
if functype == 'ellipsoid':
alpha = 0.03
else:
alpha = 0.01
if lsmode == 'golden':
alpha = 1.0
elif lsmode == 'armijo':
alpha = 1.0
# differentiation x mesh
h = 0.01
# graph plot parameters
fplot = 1
ngdata = 51
xgmin = -4.0
xgmax = 4.0
ygmin = -4.0
ygmax = 4.0
tsleep = 0.3
[ドキュメント]
def linesearch_armijo(func, x0, sdir,
xrange = None, h = None, alpha = None, dump = None, nmaxiter = None, eps = None):
xi = 0.5 # 0 < xi < 1
tau = 0.5 # 0 < tau < 1
dr2 = np.inner(sdir, sdir)
f0 = func(x0)
for i in range(nmaxiter):
dx = alpha * sdir
fp = func(x0 + dx)
fxi = f0 - xi * alpha * dr2
# print("i={:3d} alpha={:6.3f} fp={:6.3f} fxi={:6.3f}".format(i, alpha, fp, fxi), " dx=", dx)
if fp <= fxi or alpha < eps:
return i, x0 + dx, dx, alpha
else:
alpha *= tau
return -1, x0 + dx, dx, alpha
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def linesearch_golden(func, x0, sdir,
xrange = None, h = None, alpha = None, dump = None, nmaxiter = None, eps = None):
eta = 0.381966011 # (sqrt(5) - 1) / (sqrt(5) + 1)
dr2 = np.inner(sdir, sdir)
dr = sqrt(dr2)
x = [0.0, 0.0 + xrange*eta, xrange - xrange*eta, xrange]
f0 = func(x0)
f3 = func(x0 + xrange * sdir)
for i in range(nmaxiter):
alpha2 = (x[1] + x[2]) / 2.0
dx = alpha2 * sdir
if abs(x[0] - x[1]) < eps:
return i, x0 + dx, dx, alpha2
f1 = func(x0 + x[1] * sdir)
f2 = func(x0 + x[2] * sdir)
if f1 < f2 or (f0 < f1 and f0 < f2 and f0 < f3):
dxe = x[2] - x[0]
x = [x[0], x[0] + dxe * eta, x[2] - dxe * eta, x[2]]
f3 = f2
else:
dxe = x[3] - x[1]
x = [x[1], x[1] + dxe * eta, x[3] - dxe * eta, x[3]]
f0 = f1
"""
f = []
f.append(func(x0 + x[0] * sdir))
f.append(func(x0 + x[1] * sdir))
f.append(func(x0 + x[2] * sdir))
f.append(func(x0 + x[3] * sdir))
print("i=", i, " x=", x, " f=", f)
"""
return -1, x0 + dx, dx, alpha2
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def linesearch_exact(func, x0, sdir,
xrange = None, h = None, alpha = None, dump = None, nmaxiter = None, eps = None):
dr2 = np.inner(sdir, sdir)
dr = sqrt(dr2)
for i in range(nmaxiter):
fm = func(x0 + h * sdir / dr)
f0 = func(x0)
fp = func(x0 - h * sdir / dr)
f1 = (fp - fm) / 2.0 / h
f2 = (fp - 2.0 * f0 + fm) / h / h
alpha2 = f1 / (abs(f2) + dump)
# print("f1 = ", f1, " f2 = ", f2, " dr = ", dr)
dx0 = alpha2 * sdir / dr
x0 += dx0
dr0 = sqrt(np.inner(dx0, dx0))
if abs(alpha2/dr) < eps:
return i, x0, alpha2 * sdir, alpha2
return -1, x0, alpha2 * sdir, alpha2
[ドキュメント]
def linesearch_one(func, x0, sdir,
xrange = None, h = None, alpha = None, dump = None, nmaxiter = None, eps = None):
return 1, x0 + alpha * sdir, alpha * sdir, alpha
[ドキュメント]
def linesearch_simple(func, x0, sdir,
xrange = None, h = None, alpha = None, dump = None, nmaxiter = None, eps = None):
diff = -sdir
f0 = func(x0)
# print("x0=", x0, " f0=", f0)
xprev = x0.copy()
x = x0.copy()
for i in range(1, nmaxiter+1):
alpha2 = i * alpha
for j in range(len(x0)):
x[j] = x0[j] - alpha2 * diff[j]
f1 = func(x)
# print("i:{}".format(i), " x=", x, " f1=", f1)
if f1 > f0:
return i, xprev, -alpha2 * diff, alpha2
else:
xprev = x.copy()
f0 = f1
return -1, x, -alpha2 * diff, alpha2
[ドキュメント]
def searchdir_sd(x = None, diff1func = None, icg = None, gradfkm = None, dkm = None):
n = len(x0)
gradfk = np.empty(n)
for i in range(0, n):
# gradient of the target function
gradfk[i] = diff1func(i, x)
return -gradfk, icg, gradfkm, dkm
[ドキュメント]
def searchdir_cg(x = None, diff1func = None, icg = None, gradfkm = None, dkm = None):
n = len(x)
gradfk = np.empty(n)
for i in range(0, n):
# gradient of the target function
gradfk[i] = diff1func(i, x)
if icg == 0:
# search direction
dk = -gradfk
else:
yk = gradfk - gradfkm
inn1 = np.inner(gradfk, yk)
inn2 = np.inner(dkm, yk)
# search direction
dk = -gradfk + inn1 / inn2 * dkm
gradfkm = gradfk.copy()
dkm = dk.copy()
icg += 1
# reset if the number of conjugate gradients exceeds the dimension of the parameters
if icg >= n:
icg = 0
return dk, icg, gradfkm, dkm
# first derivative of func(x)
[ドキュメント]
def ediff1(i, x):
if i == 0:
return 2.0 * x[0]
if i == 1:
return 18.0 * x[1]
[ドキュメント]
def gdiff1(i, x):
if i == 0:
return - 10.0 - 60.0 * x[0] + 4.5 * x[0]*x[0] + 12.0 * x[0]*x[0]*x[0] \
+ 3.0 * x[1] * x[1]
if i == 1:
return 30.0 - 60.0 * x[1] + 12.0 * x[1]*x[1]*x[1] \
+ 6.0 * x[0] * x[1]
[ドキュメント]
def efunc(x):
return x[0]*x[0] + 9.0*x[1]*x[1]
[ドキュメント]
def gfunc(x):
return -3.0 - 10.0 * x[0] - 30.0 * x[0]*x[0] + 1.5 * x[0]*x[0]*x[0] + 3.0 * x[0]*x[0]*x[0]*x[0] \
+ 30.0 * x[1] - 30.0 * x[1]*x[1] + 3.0 * x[1]*x[1]*x[1]*x[1] \
+ 3.0 * x[0] * x[1] * x[1]
# global function variables
if functype == 'ellipsoid':
diff1 = ediff1
func = efunc
else:
diff1 = gdiff1
func = gfunc
[ドキュメント]
def main():
global plt, ngdata, xgmin, xgmax
global x0, alpha, dump, eps, nmaxiter, iprintinterval
global func, diff1
print("Find minimum point by steepest-descend / conjugate gradient methods")
print("")
# plot surface graph
if fplot == 1:
xgstep = (xgmax - xgmin) / (ngdata - 1)
ygstep = (ygmax - ygmin) / (ngdata - 1)
xg = np.empty([ngdata, ngdata])
yg = np.empty([ngdata, ngdata])
zg = np.empty([ngdata, ngdata])
for ix in range(ngdata):
for iy in range(ngdata):
xg[ix, iy] = xgmin + ix * xgstep
yg[ix, iy] = ygmin + iy * ygstep
zg[ix, iy] = func([xg[ix][iy], yg[ix][iy]])
fig = plt.figure(figsize = (10, 5))
ax = fig.add_subplot(1, 2, 1)
ax2 = fig.add_subplot(1, 2, 2, projection='3d')
# ax = fig.add_subplot(111, projection='3d')
# surf = ax.plot_surface(xg, yg, zg, cmap=cm.coolwarm)
surf = ax2.plot_wireframe(xg, yg, zg, rstride=2, cstride=2)
# ax.set_zlim3d(-3.01, 3.01)
# plt.colorbar(surf, shrink=0.5, aspect=10)
ax.set_title('contour')
contour = ax.contourf(xg, yg, zg, levels = 51, cmap = 'Spectral')
# contour = ax.contour(xg, yg, zg, levels = 51, cmap = 'Spectral')
solve, = ax.plot([], color = 'blue', marker = 'o', linestyle = '-')
# solve, = ax.plot([], color = 'blue', marker = 'o', linestyle = '')
plt.pause(0.1)
# plt.show()
# calculate initial parameters
searchdir_func = searchdir_sd
if algorism == 'cg':
searchdir_func = searchdir_cg
ls_func = linesearch_one
if lsmode == 'simple':
ls_func = linesearch_simple
elif lsmode == 'exact':
ls_func = linesearch_exact
elif lsmode == 'golden':
ls_func = linesearch_golden
elif lsmode == 'armijo':
ls_func = linesearch_armijo
n = len(x0)
gradfkm = None
dkm = None
f = func(x0)
print("x0 = ({}, {}): f = {}".format(x0[0], x0[1], f))
xt = []
yt = []
xt.append(x0[0])
yt.append(x0[1])
# counter of cg steps
icg = 0
# optimization start
for iter in range(nmaxiter):
dk, icg, gradfkm, dkm = searchdir_func(x0, diff1, icg, gradfkm, dkm)
# pass the first derivative of the target function, i.e., the negative value of the search direction dk, -dk
insiter, x0, dx, alpha2 = ls_func(func, x0, dk,
xrange = xrange, h = h, alpha = alpha, dump = dump,
nmaxiter = nmaxlsiter, eps = alphaeps)
f = func(x0)
dxmax = max(abs(dx))
print("{:4d}({:3d}): x = ({:14.6g}, {:14.6g}) dx = {:14.6g}: f = {:14.6g}".format(
iter, insiter, x0[0], x0[1], dxmax, f))
if fplot == 1:
xt.append(x0[0])
yt.append(x0[1])
solve.set_data(xt, yt)
plt.pause(0.1)
time.sleep(tsleep)
if dxmax < eps:
print("Converged at x = ", x0, " dx = {} f = {}".format(dxmax, f))
break;
else:
print("Not converged")
if fplot == 1:
print("Press enter to terminate:", end = '')
ret = input()
if __name__ == "__main__":
main()