Newton's method

The Babylonian method is an algorithm to find an approximate value for $\sqrt{k}$. It was described by the first-century Greek mathematician Hero of Alexandria.

The method starts with some initial guess, called $x_0$. It then applies a formula to produce an improved guess. This is repeated until the improved guess is accurate enough or it is clear the algorithm fails to work.

For the Babylonian method, the next guess, $x_{i+1}$ derived from the current guess, $x_i$, is:

\[ ~ x_{i+1} = \frac{1}{2}(x_i + \frac{k}{x_i}) ~ \]

We use this algorithm to approximate the square root of $2$, a value known to the Babylonians.

Start with $x$, then form $x/2 + 1/x$, from this again form $x/2 + 1/x$, repeat.

Let's look starting with $x = 2$ as a rational number:

x = 2//1
x = x//2 + 1//x
x, x^2.0

(3//2, 2.25)

Our estimate improved from something which squared to $4$ down to something which squares to $2.25$. A big improvement, but there is still more to come.

x = x//2 + 1//x
x, x^2.0

(17//12, 2.0069444444444446)

We now see accuracy until the third decimal point.

x = x//2 + 1//x
x, x^2.0

(577//408, 2.000006007304883)

This is now accurate to the sixth decimal point. That is about as far as we, or the Bablyonians, would want to go by hand. Using rational numbers quickly grows out of hand. The next step shows the explosion:

x = x//2 + 1//x

665857//470832

However, with the advent of floating point numbers, the method stays quite manageable:

x = 2.0
x = x/2 + 1/x   # 1.5, 2.25
x = x/2 + 1/x   # 1.4166666666666665, 2.006944444444444
x = x/2 + 1/x   # 1.4142156862745097, 2.0000060073048824
x = x/2 + 1/x   # 1.4142135623746899, 2.0000000000045106
x = x/2 + 1/x   # 1.414213562373095,  1.9999999999999996
x = x/2 + 1/x   # 1.414213562373095,  1.9999999999999996

1.414213562373095

We see that the algorithm - to the precision offered by floating point numbers - has resulted in an answer 1.414213562373095. This answer is an approximation to the actual answer. Approximation is necessary, as $\sqrt{2}$ is an irrational number and so can never be exactly represented in floating point. That being said, we see that the value of $f(x)$ is accurate to the last decimal place, so our approximation is very close and is achieved in a few steps.

Newton's generalization

Let $f(x) = x^3 - 2x -5$. The value of 2 is almost a zero, but not quite, as $f(2) = -1$. We can check that there are no rational roots. Though there is a method to solve the cubic it may be difficult to compute and will not be as generally applicable as some algorithm like the Babylonian method to produce an approximate answer.

Is there some generalization to the Babylonian method?

We know that the tangent line is a good approximation to the function at the point. Looking at this graph gives a hint as to an algorithm:

The tangent line and the function nearly agree near $2$. So much so, that the intersection point of the tangent line with the $x$ axis nearly hides the actual zero of $f(x)$ that is near $2.1$.

That is, it seems that the intersection of the tangent line and the $x$ axis should be an improved approximation for the zero of the function.

Let $x_0$ be $2$, and $x_1$ be the intersection point of the tangent line at $(x_0, f(x_0))$ with the $x$ axis. Then by the definition of the tangent line:

\[ ~ f'(x_0) = \frac{\Delta y }{\Delta x} = \frac{f(x_0)}{x_0 - x_1}. ~ \]

This can be solved for $x_1$ to give $x_1 = x_0 - f(x_0)/f'(x_0)$. In general, if we had $x_i$ and used the intersection point of the tangent line to produce $x_{i+1}$ we would have Newton's method:

\[ ~ x_{i+1} = x_i - \frac{f(x_i)}{f'(x_i)}. ~ \]

We will use automatic derivatives, as possible, so load the CalculusWithJulia package which provides the f' notation for derivatives through the definition Base.adjoint(f::Function)=x->ForwardDiff.derivative(f, float(x)):

using CalculusWithJulia
using Plots

With this, the algorithm above starting from $2$ becomes:

x0 = 2
x1 = x0 - f(x0)/f'(x0)

2.1

We can see we are closer to a zero:

f(x0), f(x1)

(-1, 0.06100000000000083)

Trying again, we have

x2 = x1 - f(x1)/ f'(x1)
x2, f(x2), f(x1)

(2.094568121104185, 0.00018572317327247845, 0.06100000000000083)

And again:

x3 = x2 - f(x2)/ f'(x2)
x3, f(x3), f(x2)

(2.094551481698199, 1.7397612239733462e-9, 0.00018572317327247845)

x4 = x3 - f(x3)/ f'(x3)
x4, f(x4), f(x3)

(2.0945514815423265, -8.881784197001252e-16, 1.7397612239733462e-9)

We see now that $f(x_4)$ is within machine tolerance of $0$, so we call $x_4$ an approximate zero of $f(x)$.

Newton's method. Let $x_0$ be an initial guess for a zero of $f(x)$. Iteratively define $x_{i+1}$ in terms of the just generated $x_i$ by: $x_{i+1} = x_i - f(x_i) / f'(x_i)$. Then for most functions and reasonable initial guesses, the sequence of points converges to a zero of $f$.

On the computer, we know that actual convergence will likely never occur, but accuracy to a certain tolerance can often be achieved.

In the example above, we kept track of the previous values. This is unnecessary if only the answer is sought. In that case, the update step can use the same variable:

x = 2                     # x0
x = x - f(x) / f'(x)      # x1
x = x - f(x) / f'(x)      # x2
x = x - f(x) / f'(x)      # x3
x = x - f(x) / f'(x)      # x4

2.0945514815423265

As seen above, the assignment will update the value bound to x using the previous value of x in the computation.

We implement the algorithm by repeating the step until either we converge or it is clear we won't converge. For good guesses and most functions, convergence happens quickly.

Examples

Example: visualizing convergence

This graphic demonstrates the method and the rapid convergence:

Example: numeric not algebraic

For the function $f(x) = \cos(x) - x$, we see that SymPy can not solve symbolically for a zero:

@vars x real=true
solve(cos(x) - x, x)

ERROR: PyError ($(Expr(:escape, :(ccall(#= /Users/verzani/.julia/packages/PyCall/zqDXB/src/pyfncall.jl:43 =# @pysym(:PyObject_Call), PyPtr, (PyPtr, PyPtr, PyPtr), o, pyargsptr, kw))))) <class 'NotImplementedError'>
NotImplementedError('multiple generators [x, cos(x)]\nNo algorithms are implemented to solve equation -x + cos(x)')
  File "/Users/verzani/.julia/conda/3/lib/python3.7/site-packages/sympy/solvers/solvers.py", line 1174, in solve
    solution = _solve(f[0], *symbols, **flags)
  File "/Users/verzani/.julia/conda/3/lib/python3.7/site-packages/sympy/solvers/solvers.py", line 1748, in _solve
    raise NotImplementedError('\n'.join([msg, not_impl_msg % f]))

We can find a numeric solution, even though there is no closed-form answer. Here we try Newton's method:

f(x) = cos(x) - x
x = .5
x = x - f(x)/f'(x)  # 0.7552224171056364
x = x - f(x)/f'(x)  # 0.7391416661498792
x = x - f(x)/f'(x)  # 0.7390851339208068
x = x - f(x)/f'(x)  # 0.7390851332151607
x = x - f(x)/f'(x)

0.7390851332151607

This answer is close, to machine tolerance it produces $0.0$:

x, f(x)

(0.7390851332151607, 0.0)

Example division as multiplication

Newton-Raphson Division is a means to divide by multiplying.

Why would you want to do that? Well, even for computers division is harder (read slower) than multiplying. The trick is that $p/q$ is simply $p \cdot (1/q)$, so finding a means to compute a reciprocal by multiplying will reduce division to multiplication. (This trick is used by yeppp, a high performance library for computational mathematics.)

Well suppose we have $q$, we could try to use Newton's method to find $1/q$, as it is a solution to $f(x) = x - 1/q$. The Newton update step simplifies to:

\[ ~ x - f(x) / f'(x) \quad\text{or}\quad x - (x - 1/q)/ 1 = 1/q ~ \]

That doesn't really help, as Newton's method is just $x_{i+1} = 1/q$

• that is it just jumps to the answer, the one we want to compute by

some other means!

Trying again, we simplify the update step for a related function: $f(x) = 1/x - q$ with $f'(x) = -1/x^2$ and then one step of the process is:

\[ ~ x_{i+1} = x_i - (1/x_i - q)/(-1/x_i^2) = -qx^2_i + 2x_i. ~ \]

Now for $q$ in the interval $[1/2, 1]$ we want to get a good initial guess. Here is a claim. We can use $x_0=48/17 - 32/17 \cdot q$. Let's check graphically that this is a reasonable initial approximation to $1/q$:

g(q) = 1/q
h(q) = 1/17 * (48 - 32q)
plot(g, 1/2, 1)
plot!(h)

It can be shown that we have for any $q$ in $[1/2, 1]$ with initial guess $x_0 = 48/17 - 32/17\cdot q$ that Newton's method will converge to 16 digits in no more than this many steps:

\[ ~ \log_2(\frac{53 + 1}{\log_2(17)}). ~ \]

a = log2((53 + 1)/log2(17))
ceil(Integer, a)

4

That is 4 steps suffices.

For $q = 0.80$, to find $1/q$ using the above we have

q = 0.80
x = (48/17) - (32/17)*q
x = -q*x*x + 2*x
x = -q*x*x + 2*x
x = -q*x*x + 2*x
x = -q*x*x + 2*x

1.25

This method has basically $18$ multiplication and addition operations for one division, so it naively would seem slower, but timing this shows the method is competitive with a regular division.

A function

In the previous example, a bound ensures convergence in 4 steps. In general, this is not the case with Newton's method where the algorithm is iterated until convergence. Having to repeat steps until something happens is a task best done by the computer. The while loop is a good way to repeat commands until some condition is met. With this, we present a simple function implementing Newton's method, we iterate until the update step gets really small (the delta) or the convergence takes more than 50 steps. (There are other reasonable choices that could be used to determine when the algorithm should stop.)

function nm(f, fp, x0)
  tol = 1e-14
  ctr = 0
  delta = Inf
  while (abs(delta) > tol) & (ctr < 50)
    delta = f(x0)/fp(x0)
    x0 = x0 - delta
    ctr = ctr + 1
  end

  ctr < 50 ? x0 : NaN
end

nm (generic function with 1 method)

Examples

• Find a zero of $\sin(x)$ starting at $x_0=3$:

nm(sin, cos, 3)

3.141592653589793

This is an approximation for $\pi$, that historically found use, as the convergence is fast.

• Find a solution to $x^5 = 5^x$ near $2$:

Writing a function to handle this, we have:

f(x) = x^5 - 5^x

f (generic function with 1 method)

We can find the derivative, but in this example will let the D function from the Roots package do so for us:

alpha = nm(f, f', 2)
alpha, f(alpha)

(1.764921914525776, 0.0)

Functions in the Roots package

Typing in the nm function might be okay once, but would be tedious if it was needed each time. The Roots package provides a Newton method. Roots is loaded with

using Roots
find_zero((sin, cos), 3, Roots.Newton())  # alternatively Roots.newton(sin,cos, 3)

3.141592653589793

Or, if a derivative is not specified, one can be computed using automatic differentiation:

find_zero((f, f'), 2, Roots.Newton())

1.764921914525776

The argument verbose=true will force a print out of a message summarizing each step.

More generally, the function find_zero provides a derivative-free algorithm for finding roots of functions, when started with an initial guess. It is similar to Newton's method in that only a good initial guess is needed. However, the algorithm, while slower in terms of function evaluations and steps, is engineered to be a bit more robust to the choice of initial estimate than Newton's method. (If it finds a bracket, it will use a bisection algorithm which is guaranteed to converge, but can be slower to do so.) Here we see how to call the function:

f(x) = cos(x) - x
x0 = 1
find_zero(f, x0)

0.7390851332151607

Compare to this related call which uses the bisection method:

find_zero(f, (0, 1))           ## [0,1] must be a bracketing interval

0.7390851332151607

For this example both give the same answer, but the bisection method is a bit more inconvenient as a bracketing interval must be pre-specified.

Example: intersection of two graphs

Find the intersection point between $f(x) = \cos(x)$ and $g(x) = 5x$ near $0$.

We have Newton's method to solve for zeros of $f(x)$, i.e. when $f(x) = 0$. Here we want to solve for $x$ with $f(x) = g(x)$. To do so, we make a new function $h(x) = f(x) - g(x)$, for that is $0$ when $f(x)$ equals $g(x)$:

f(x) = cos(x)
g(x) = 5x
h(x) = f(x) - g(x)
x0 = find_zero((h,h'), 0, Roots.Newton())
x0, h(x0), f(x0), g(x0)

(0.19616428118784215, 0.0, 0.9808214059392107, 0.9808214059392107)

Example: Finding $c$ in Rolle's Theorem

The function $f(x) = \sqrt{1 - \cos(x^2)^2}$ has a zero at $0$ and one near 1.77.

f(x) = sqrt(1 - cos(x^2)^2)
plot(f, 0, 1.77)

As $f(x)$ is differentiable between $0$ and $a$, Rolle's theorem says there will be value where the derivative is $0$. Find that value.

This value will be a zero of the derivative. A graph shows it should be near $1.2$, so we use that as a starting value to get the answer:

find_zero(f', 1.2)

1.2533141373155003

Convergence

Newton's method is famously known to have "quadratic convergence." What does this mean? Let the error in the $i$th step be called $e_i = x_i - \alpha$. Then Newton's method satisfies a bound of the type:

\[ ~ \lvert e_{i+1} \rvert \leq M_i \cdot e_i^2. ~ \]

If $M$ were just a constant and we suppose $e_0 = 10^{-1}$ then $e_1$ would be less than $M 10^{-2}$ and $e_2$ less than $M^2 10^{-4}$, $e_3$ less than $M^3 10^{-8}$ and $e_4$ less than $M^4 10^{-16}$ which for $M=1$ is basically the machine precision. That is for some problems, with a good initial guess it will take around 4 or so steps to converge.

The actual value of $M$ depends on $i$ and $f$, so the answer isn't always so easy. To see what $M$ is, the basic assumption of $f$ is such that this fact of linearization holds at each $x_i$ with $f(x_i) \neq 0$:

\[ ~ f(x) = f(x_i) + f'(x_i) \cdot (x - x_i) + \frac{1}{2} f''(\xi) \cdot (x-x_i)^2. ~ \]

The value $\xi$ is from the mean value theorem and is between $x$ and $x_i$.

Let $x=\alpha$, the zero of $f(x)$ that is being sought. Then $f(\alpha)=0$ and $0=f(x_i)/f'(x_i) + (\alpha-x_i) + 1/2\cdot f''(\xi)/f'(x_i) \cdot (\alpha-x_i)^2$. For this value, we have

\[ ~ x_{i+1} - \alpha = x_i - \frac{f(x_i)}{f'(x_i)} - \alpha = (x_i - \alpha) + (\alpha - x_i) + \frac{1}{2}\frac{f''(\xi) \cdot(\alpha - x_i)^2}{f'(x_i)} = \frac{1}{2}\frac{f''(\xi)}{f'(x_i)} \cdot(x_i - \alpha)^2. ~ \]

That is

\[ ~ \lvert e_{i+1}\rvert \leq \frac{1}{2}\frac{\lvert f''(\xi)\rvert}{\lvert f'(x_i)\rvert} e_i^2. ~ \]

This convergence will be quadratic if:

• The initial guess $x_0$ is not too far from $\alpha$, so $e_0$ is managed.

• The derivative at $x_i$ is not too close to $0$. (As it appears in the denominator). That is, the function can't be too flat, which should make sense, as then the tangent line is nearly parallel to the $x$ axis and would intersect far away.

• The second derivative is not too big (in absolute value) near the zero. A large second derivative means the function is very concave, which means it is "turning" a lot. In this case, the function turns away from the tangent line quickly, so the tangent line's zero is not necessarily a better approximation to the actual zero, $\alpha$.

What can go wrong when one of these isn't the case is illustrated next:

Poor initial step

The second derivative is too big

The tangent line at some $x_i$ is flat

Example

Suppose $\alpha$ is a simple zero for $f(x)$. (The value $\alpha$ is a zero of multiplicity $k$ if $f(x) = (x-\alpha)^kg(x)$ where $g(\alpha)$ is not zero.) A simple zero has multiplicity $1$. If $f'(\alpha) \neq 0$ and the second derivative exists, then a zero $\alpha$ will be simple.) Around $\alpha$, quadratic convergence should apply. However, consider the function $g(x) = f(x)^k$ for some integer $k \geq 2$. Then $\alpha$ is still a zero, but the derivative of $g$ at $\alpha$ is zero, so the tangent line is basically flat. This will slow the convergence up. We can see that the update step $g'(x)/g(x)$ becomes $(1/k) f'(x)/f(x)$, so an extra factor is introduced.

The calculation that produces the quadratic convergence now becomes:

\[ ~ x_{i+1} - \alpha = (x_i - \alpha) - \frac{1}{k}(x_i-\alpha + \frac{f''(\xi)}{2f'(x_i)}(x_i-\alpha)^2) = \frac{k-1}{k} (x_i-\alpha) + \frac{f''(\xi)}{2kf'(x_i)}(x_i-\alpha)^2. ~ \]

As $k > 1$, the $(x_i - \alpha)$ term dominates, and we see the convergence is linear with $\lvert e_{i+1}\rvert \approx (k-1)/k \lvert e_i\rvert$.

Questions

Question

Look at this graph with $x_0$ marked with a point:

If one step of Newton's method was used, what would be the value of $x_1$?

Question

Look at this graph of some concave up $f(x)$ with initial point $x_0$ marked. Let $c$ be the zero.

What can be said about $x_1$?

Question

Look at this graph of some concave up $f(x)$ with initial point $x_0$ marked. Let $c$ be the zero.

What can be said about $x_1$?

Question

Suppose $f(x)$ is concave up and we have the tangent line representation: $f(x) = f(c) + f'(c)\cdot(x-c) + f''(\xi)/2 \cdot(x-c)^2$. Explain why it must be that the graph of $f(x)$ lies on or above the tangent line.

Question

Let $f(x) = x^2 - 3^x$. This has derivative $2x - 3^x \cdot \log(3)$. Starting with $x_0=0$, what does Newton's method converge on?

Question

Let $f(x) = \exp(x) - x^4$. There are 3 zeros for this function. Which one does Newton's method converge to when $x_0=2$?

Question

Let $f(x) = \exp(x) - x^4$. As mentioned, there are 3 zeros for this function. Which one does Newton's method converge to when $x_0=8$?

Question

Let $f(x) = \sin(x) - \cos(4\cdot x)$.

Starting at $\pi/8$, solve for the root returned by Newton's method

Question

Using Newton's method find a root to $f(x) = \cos(x) - x^3$ starting at $x_0 = 1/2$.

Question

Use Newton's method to find a root of $f(x) = x^5 + x -1$. Make a quick graph to find a reasonable starting point.

Question

Will Newton's method converge for the function $f(x) = x^5 - x + 1$ starting at $x=1$?

Question

Will Newton's method converge for the function $f(x) = 4x^5 - x + 1$ starting at $x=1$?

Question

Will Newton's method converge for the function $f(x) = x^{10} - 2x^3 - x + 1$ starting from $0.25$?

Question

Will Newton's method converge for $f(x) = 20x/(100 x^2 + 1)$ starting at $0.1$?

Question

Will Newton's method converge to a zero for $f(x) = \sqrt{(1 - x^2)^2}$?

Question

Use find_zero to find a root of $f(x) = 4x^4 - 5x^3 + 4x^2 -20x -6$ starting at $x_0 = 0$.

Question

Use find_zero to find a zero of $f(x) = \sin(x) - x/2$ that is bigger than $0$.

Question

The Newton baffler (defined below) is so named, as Newton's method will fail to find the root for most starting points.

function newton_baffler(x)
    if ( x - 0.0 ) < -0.25
        0.75 * ( x - 0 ) - 0.3125
    elseif  ( x - 0 ) < 0.25
        2.0 * ( x - 0 )
    else
        0.75 * ( x - 0 ) + 0.3125
    end
end

newton_baffler (generic function with 1 method)

Will find_zero find the zero at $0.0$ starting at 1 using the default option for order?

Will Newton's method find the zero at $0.0$ starting at $1$?

Considering this plot:

plot(newton_baffler, -1.1, 1.1)

Starting with $x_0=1$, you can see why Newton's method will fail. Why?

Question

Consider this crazy function defined by:

import SpecialFunctions: erf
f(x) = cos(100*x)-4*erf(30*x-10)

f (generic function with 1 method)

(The erf function is the error function.)

Make a plot over the interval $[-3,3]$ to see why it is called "crazy".

Does find_zero find a zero to this function starting from $0$?

If so, what is the value?

If not, what is the reason?

Does find_zero find a zero to this function starting from $1$?

If so, what is the value?

If not, what is the reason?

Question

Let $f(x) = \sin(x) - x/4$. Starting at $x_0 = 2\pi$ Newton's method will converge to a value, but it will take many steps. Using verbose=true when calling the newton function in the Roots package, how many steps does it take:

What is the zero that is found?

Is this the closest zero to the starting point, $x_0$?

Question

Quadratic convergence of Newton's method only applies to simple roots. For example, we can see (using the verbose=true argument to the Roots package's newton method, that it only takes $4$ steps to find a zero to $f(x) = \cos(x) - x$ starting at $x_0 = 1$. But it takes many more steps to find the same zero for $f(x) = (\cos(x) - x)^2$.

How many?

Question: implicit equations

The equation $x^2 + x\cdot y + y^2 = 1$ is a rotated ellipse.

Can we find which point on its graph has the largest $y$ value?

This would be straightforward if we could write $y(x) = \dots$, for then we would simply find the critical points and investiate. But we can't so easily solve for $y$ interms of $x$. However, we can use Newton's method to do so:

function findy(x)
  fn = y -> (x^2 + x*y + y^2) - 1
  fp = y -> (x + 2y)
  find_zero((fn, fp), sqrt(1 - x^2), Roots.Newton())
end

findy (generic function with 1 method)

For a fixed x, this solves for $y$ in the equation: $F(y) = x^2 + x \cdot y + y^2 - 1 = 0$. It should be that $(x,y)$ is a solution:

x = .75
y = findy(x)
x^2 + x*y + y^2  ## is this 1?

1.0000000000000002

So we have a means to find $y(x)$, but it is implicit. We can't readily find the derivative to find critical points. Instead we can use the approximate derivative with $h=10^{-6}$:

yp(x) = (findy(x + 1e-6) - findy(x)) / 1e-6

yp (generic function with 1 method)

Using find_zero, find the value $x$ which maximizes yp. Use this to find the point $(x,y)$ with largest $y$ value.

Question

In the last problem we used an approximate derivative in place of the derivative. This can introduce an error due to the approximation. Will this be true if we replace the derivative in Newton's method with an approximation? In general, this can often be done but the convergence can be slower and the sensitivity to a poor initial guess even greater.

Three common approximations are given by the difference quotient for a fixed $h$: $f'(x_i) \approx (f(x_i+h)-f(x_i))/h$; the secant line approximation: $f'(x_i) \approx (f(x_i) - f(x_{i-1})) / (x_i - x_{i-1})$; and the Steffensen approximation $f'(x_i) \approx (f(x_i + f(x_i)) - f(x_i)) / f(x_i)$ (using $h=f(x_i)$).

Let's revisit the $4$-step convergence of Newton's method to the root of $f(x) = 1/x - q$ when $q=0.8$. Will these methods be as fast?

q = 0.8
xstar = 1.25 # q = 4/5 --> 1/q = 5/4
f(x) = 1/x - q

f (generic function with 2 methods)

Let's define the above approximations for a given f:

delta = 1e-6
secant_approx(x0,x1) = (f(x1) - f(x0)) / (x1 - x0)
diffq_approx(x0, h) = secant_approx(x0, x0+h)
steff_approx(x0) = diffq_approx(x0, f(x0))

steff_approx (generic function with 1 method)

Then using the difference quotient would look like:

x1 = 42/17 - 32/17*q
x1 = x1 - f(x1) / diffq_approx(x1, delta)   # |x1 - xstar| = 0.06511395862036995
x1 = x1 - f(x1) / diffq_approx(x1, delta)   # |x1 - xstar| = 0.003391809999860218; etc

1.2466081900001398

The Steffensen method would look like:

x1 = 42/17 - 32/17*q
x1 = x1 - f(x1) / steff_approx(x1)   # |x1 - xstar| = 0.011117056291670258
x1 = x1 - f(x1) / steff_approx(x1)   # |x1 - xstar| = 3.502579696146313e-5; etc.

1.2499649742030385

And the secant method like:

x1 = 42/17 - 32/17*q
x0 = x1 - delta # we need two initial values
x0, x1 = x1, x1 - f(x1) / secant_approx(x0, x1)   # |x1 - xstar| = 8.222358365284066e-6
x0, x1 = x1, x1 - f(x1) / secant_approx(x0, x1)   # |x1 - xstar| = 1.8766323799379592e-6; etc.

(1.1848855848819007, 1.235138592314222)

Repeat each of the above algorithms until abs(x1 - 1.25) is 0 (which will happen for this problem, though not in general). Record the steps.

• Does the difference quotient need more than $4$ steps?

• Does the secant method need more than $4$ steps?

• Does the Steffensen method need more than 4 steps?

All methods work quickly with this well-behaved problem. In general the convergence rates are slightly different for each, with the Steffensen method matching Newton's method and the difference quotient method being slower in general. All can be more sensitive to the initial guess.