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NonLinearEquationsLib.py
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Wed, May 1, 15:26
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text/x-python
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rPUBNUMANALYSIS Public Numerical Analysis Jupyter Notebook
NonLinearEquationsLib.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 30
@author: Simone Deparis
"""
import
numpy
as
np
def
Bisection
(
a
,
b
,
fun
,
tolerance
,
maxIterations
)
:
# [a,b] interval of interest
# fun function
# tolerance desired accuracy
# maxIterations : maximum number of iteration
# returns:
# zero, residual, number of iterations
if
(
a
>=
b
)
:
print
(
' b must be greater than a (b > a)'
)
return
0
,
0
,
0
# what we consider as "zero"
eps
=
1e-12
# evaluate f at the endpoints
fa
=
fun
(
a
)
fb
=
fun
(
b
)
if
abs
(
fa
)
<
eps
:
# a is the solution
zero
=
a
esterr
=
fa
k
=
0
return
zero
,
esterr
,
k
if
abs
(
fb
)
<
eps
:
# b is the solution
zero
=
b
esterr
=
fb
k
=
0
return
zero
,
esterr
,
k
if
fa
*
fb
>
0
:
print
(
' The sign of FUN at the extrema of the interval must be different'
)
return
0
,
0
,
0
# We want the final error to be smaller than tol,
# i.e. k > log( (b-a)/tol ) / log(2)
nmax
=
int
(
np
.
ceil
(
np
.
log
(
(
b
-
a
)
/
tol
)
/
np
.
log
(
2
)))
# but nmax shall be smaller the the nmaximum iterations asked by the user
if
(
maxIterations
<
nmax
)
:
nmax
=
int
(
round
(
maxIterations
))
print
(
'Warning: nmax is smaller than the minimum number of iterations necessary to reach the tolerance wished'
);
# vector of intermadiate approximations etc
x
=
np
.
zeros
(
nmax
)
# initial error is the length of the interval.
esterr
=
(
b
-
a
)
# do not need to store all the a^k and b^k, so I call them with a new variable name:
ak
=
a
bk
=
b
# the values of f at those points are fa and fk
for
k
in
range
(
nmax
)
:
# approximate solution is midpoint of current interval
x
[
k
]
=
(
ak
+
bk
)
/
2
fx
=
fun
(
x
[
k
]);
# error estimator is the half of the previous error
esterr
=
esterr
/
2
# if we found the solution, stop the algorithm
if
np
.
abs
(
fx
)
<
eps
:
# error is zero
zero
=
x
[
k
]
esterr
=
0
;
return
zero
,
esterr
,
k
if
fx
*
fa
<
0
:
# alpha is in (a,x)
bk
=
x
[
k
]
elif
fx
*
fb
<
0
:
# alpha is in (x,b)
ak
=
x
[
k
]
else
:
error
(
'Algorithm not operating correctly'
)
zero
=
x
[
k
];
if
esterr
>
tol
:
print
(
'Warning: bisection stopped without converging to the desired tolerance because the maximum number of iterations was reached'
);
return
zero
,
esterr
,
k
def
FixedPoint
(
phi
,
x0
,
a
,
b
,
tol
,
nmax
)
:
'''
FixedPoint Find the fixed point of a function by iterative iterations
FixedPoint( PHI,X0,a,b, TOL,NMAX) tries to find the fixedpoint X of the a
continuous function PHI nearest to X0 using
the fixed point iterations method.
[a,b] : if the iterations exit the interval, the method stops
If the search fails an error message is displayed.
Outputs : [x, r, n, inc, x_sequence]
x : the approximated fixed point of the function
r : the absolute value of the residual in X : |phi(x) - x|
n : the number of iterations N required for computing X and
x_sequence : the sequence computed by Newton
'''
# Initial values
n
=
0
xk
=
x0
# initialisation of loop components
# increments (in abs value) at each iteration
inc
=
[]
# in case we wish to plot the sequence
x
=
[
x0
]
# diff : last increment,
diff
=
tol
+
1
# initially set larger then tolerance
# Loop until tolerance is reached
while
(
diff
>=
tol
and
n
<=
nmax
and
(
xk
>=
a
)
and
(
xk
<=
b
)
)
:
# call phi
xk1
=
phi
(
xk
)
# increments
diff
=
np
.
abs
(
xk1
-
xk
)
# prepare the next loop
n
=
n
+
1
xk
=
xk1
x
.
append
(
xk
)
# Final residual
rk1
=
np
.
abs
(
xk1
-
phi
(
xk1
))
# Warning if not converged
if
n
>
nmax
:
print
(
'FixexPoint stopped without converging to the desired tolerance '
)
print
(
'because the maximum number of iterations was reached'
)
return
xk1
,
rk1
,
n
,
np
.
array
(
x
)
def
plotPhi
(
a
,
b
,
phi
,
label
=
'$\phi$'
,
N
=
100
)
:
'''
simple plot of fonction with bisectrice of first quadrant
useful for preparing grpahics of fixed point iterations
[a,b] : interval, used for both x and y axis
phi : funciton to plot
label : label for the fonction, usually '$\phi$'
N : number of points for plotting
'''
import
matplotlib.pyplot
as
plt
z
=
np
.
linspace
(
a
,
b
,
N
)
plt
.
plot
(
z
,
phi
(
z
),
'k-'
)
plt
.
plot
([
a
,
b
],[
a
,
b
],
':'
,
linewidth
=
0.5
)
plt
.
xlabel
(
'x'
);
plt
.
ylabel
(
label
);
# Plot the x,y-axis
plt
.
plot
([
a
,
b
],
[
0
,
0
],
'k-'
,
linewidth
=
0.1
)
plt
.
plot
([
0
,
0
],
[
a
,
b
],
'k-'
,
linewidth
=
0.1
)
plt
.
legend
([
label
,
'y=x'
])
plt
.
title
(
'Graph de la fonction '
+
label
)
def
plotPhiIterations
(
x
)
:
# plot the graphical interpretation of the Fixed Point method
import
matplotlib.pyplot
as
plt
plt
.
plot
([
x
[
0
],
x
[
0
]],
[
0
,
x
[
1
]],
'g:'
)
for
k
in
range
(
x
.
size
-
2
)
:
plt
.
plot
([
x
[
k
],
x
[
k
+
1
]],
[
x
[
k
+
1
],
x
[
k
+
1
]],
'g:'
)
plt
.
plot
([
x
[
k
+
1
],
x
[
k
+
1
]],
[
x
[
k
+
1
],
x
[
k
+
2
]],
'g:'
)
k
=
x
.
size
-
2
plt
.
plot
([
x
[
k
],
x
[
k
+
1
]],
[
x
[
k
+
1
],
x
[
k
+
1
]],
'g:'
)
# Putting a sign at the initial point
deltaAxis
=
plt
.
gca
()
.
axes
.
get_ylim
()[
1
]
-
plt
.
gca
()
.
axes
.
get_ylim
()[
0
]
if
(
x
[
1
]
<
0
)
:
plt
.
annotate
(
"$x_0$"
,
(
x
[
0
],
0.02
*
deltaAxis
)
)
else
:
plt
.
annotate
(
"$x_0$"
,
(
x
[
0
],
-
0.05
*
deltaAxis
)
)
def
plotNewtonIterations
(
a
,
b
,
f
,
x
,
N
=
200
)
:
# plot the graphical interpretation of the Newton method
import
matplotlib.pyplot
as
plt
z
=
np
.
linspace
(
a
,
b
,
N
)
plt
.
plot
(
z
,
f
(
z
),
'b-'
,
x
,
f
(
x
),
'rx'
)
# Putting a sign at the initial point
deltaAxis
=
plt
.
gca
()
.
axes
.
get_ylim
()[
1
]
-
plt
.
gca
()
.
axes
.
get_ylim
()[
0
]
# plot the graphical interpretation of the Newton method
plt
.
plot
([
x
[
0
],
x
[
0
]],
[
0
,
f
(
x
[
0
])],
'g:'
)
for
k
in
range
(
x
.
size
-
1
)
:
plt
.
plot
([
x
[
k
],
x
[
k
+
1
]],
[
f
(
x
[
k
]),
0
],
'g-'
)
plt
.
plot
([
x
[
k
+
1
],
x
[
k
+
1
]],
[
0
,
f
(
x
[
k
+
1
])],
'g:'
)
# Putting a sign at the initial point
if
(
f
(
x
[
k
])
<
0
)
:
plt
.
annotate
(
"$x_"
+
str
(
k
)
+
"$"
,
(
x
[
k
],
0.02
*
deltaAxis
)
)
else
:
plt
.
annotate
(
"$x_"
+
str
(
k
)
+
"$"
,
(
x
[
k
],
-
0.05
*
deltaAxis
)
)
plt
.
ylabel
(
'$f$'
);
plt
.
xlabel
(
'$x$'
);
# Plot the x,y-axis
plt
.
plot
([
a
,
b
],
[
0
,
0
],
'k-'
,
linewidth
=
0.1
)
plt
.
plot
([
0
,
0
],
[
np
.
min
(
f
(
z
)),
np
.
max
(
f
(
z
))],
'k-'
,
linewidth
=
0.1
)
plt
.
legend
([
'$f$'
,
'($x_k$,$f(x_k)$)'
])
def
plotBisectionIterations
(
a
,
b
,
f
,
x
,
N
=
200
)
:
# plot the graphical interpretation of the Bisection method
import
matplotlib.pyplot
as
plt
def
putSign
(
y
,
f
,
text
)
:
if
(
f
(
y
)
<
0
)
:
plt
.
annotate
(
text
,
(
y
,
0.02
*
deltaAxis
)
)
else
:
plt
.
annotate
(
text
,
(
y
,
-
0.05
*
deltaAxis
)
)
z
=
np
.
linspace
(
a
,
b
,
N
)
plt
.
plot
(
z
,
f
(
z
),
'b-'
)
plt
.
plot
([
a
,
a
],
[
0
,
f
(
a
)],
'g:'
)
plt
.
plot
([
b
,
b
],
[
0
,
f
(
b
)],
'g:'
)
# For putting a sign at the initial point
deltaAxis
=
plt
.
gca
()
.
axes
.
get_ylim
()[
1
]
-
plt
.
gca
()
.
axes
.
get_ylim
()[
0
]
putSign
(
a
,
f
,
"a"
)
putSign
(
b
,
f
,
"b"
)
for
k
in
range
(
x
.
size
)
:
plt
.
plot
([
x
[
k
],
x
[
k
]],
[
0
,
f
(
x
[
k
])],
'g:'
)
putSign
(
x
[
k
],
f
,
"$x_"
+
str
(
k
)
+
"$"
)
# Plot the x,y-axis
plt
.
plot
([
a
,
b
],
[
0
,
0
],
'k-'
,
linewidth
=
0.1
)
plt
.
plot
([
0
,
0
],
[
np
.
min
(
f
(
z
)),
np
.
max
(
f
(
z
))],
'k-'
,
linewidth
=
0.1
)
plt
.
ylabel
(
'$f$'
);
plt
.
xlabel
(
'$x$'
);
return
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