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Root finding functions for Julia

This package contains simple routines for finding roots of continuous scalar functions of a single real variable. The find_zerofunction provides the primary interface. It supports various algorithms through the specification of a method. These include:

Bisection-like algorithms. For functions where a bracketing interval is known (one where f(a) and f(b) have alternate signs), the Bisection method can be specified. For most floating point number types, bisection occurs in a manner exploiting floating point storage conventions. For others, an algorithm of Alefeld, Potra, and Shi is used. These methods are guaranteed to converge.
Several derivative-free methods are implemented. These are specified through the methods Order0, Order1 (the secant method), Order2 (the Steffensen method), Order5, Order8, and Order16. The number indicates, roughly, the order of convergence. The Order0 method is the default, and the most robust, but may take many more function calls to converge. The higher order methods promise higher order (faster) convergence, though don't always yield results with fewer function calls than Order1 or Order2. The methods Roots.Order1B and Roots.Order2B are superlinear and quadratically converging methods independent of the multiplicity of the zero.
There are historic methods that require a derivative or two: Roots.Newton and Roots.Halley. Roots.Schroder provides a quadratic method, like Newton's method, which is independent of the multiplicity of the zero.
There are several non-exported methods, such as, Roots.Brent(), FalsePosition, Roots.A42, Roots.AlefeldPotraShi, Roots.LithBoonkkampIJzermanBracket, and Roots.LithBoonkkampIJzerman.

Each method's documentation has additional detail.

Some examples:

using Roots
f(x) = exp(x) - x^4

# a bisection method has the bracket specified with a tuple, vector, or iterable
julia> find_zero(f, (8,9), Bisection())
8.613169456441398

julia> find_zero(f, (-10, 0))  # Bisection is default if x is a tuple and no method
-0.8155534188089606


julia> find_zero(f, (-10, 0), FalsePosition())  # just 11 function evaluations
-0.8155534188089607

For non-bracketing methods, the initial position is passed in as a scalar:

## find_zero(f, x0::Number) will use Order0()
julia> find_zero(f, 3)         # default is Order0()
1.4296118247255556

julia> find_zero(f, 3, Order1()) # same answer, different method
1.4296118247255556

julia> find_zero(sin, BigFloat(3.0), Order16())
3.141592653589793238462643383279502884197169399375105820974944592307816406286198

The find_zero function can be used with callable objects:

using SymEngine
@vars x
find_zero(x^5 - x - 1, 1.0)  # 1.1673039782614185

Or,

using Polynomials
x = variable()
find_zero(x^5 - x - 1, 1.0)  # 1.1673039782614185

The function should respect the units of the Unitful package:

using Unitful
s = u"s"; m = u"m"
g = 9.8*m/s^2
v0 = 10m/s
y0 = 16m
y(t) = -g*t^2 + v0*t + y0
find_zero(y, 1s)      # 1.886053370668014 s

Newton's method can be used without taking derivatives by hand. For example, if the ForwardDiff package is available:

using ForwardDiff
D(f) = x -> ForwardDiff.derivative(f,float(x))

Now we have:

f(x) = x^3 - 2x - 5
x0 = 2
find_zero((f,D(f)), x0, Roots.Newton())   # 2.0945514815423265

Automatic derivatives allow for easy solutions to finding critical points of a function.

## mean
using Statistics
as = rand(5)

function M(x)
  sum([(x-a)^2 for a in as])
end

find_zero(D(M), .5) - mean(as)	  # 0.0

## median
function m(x)
  sum([abs(x-a) for a in as])
end

find_zero(D(m), (0, 1)) - median(as)	# 0.0

The CommonSolve interface

The (DifferentialEquations)[https://github.com/SciML/DifferentialEquations.jl] interface of setting up a problem; initializing the problem; then solving the problem is also implemented using the methods ZeroProblem, init, solve! and solve.

For example, we can solve a problem with many different methods, as follows:

julia> f(x) = exp(-x) - x^3
f (generic function with 1 method)

julia> x0 = 2.0
2.0

julia> fx = ZeroProblem(f,x0)
ZeroProblem{typeof(f), Float64}(f, 2.0)

julia> solve(fx)
0.7728829591492101

With no default, and a single initial point specified, the default Order1 method is used. The solve method allows other root-solving methods to be passed, along with other options. For example, to use the Order2 method using a convergence criteria (see below) that |xₙ - xₙ₋₁| ≤ δ, we could make this call:

julia> solve(fx, Order2(), atol=0.0, rtol=0.0)
0.7728829591492101

Unlike find_zero, which errors on non-convergence, solve returns NaN on non-convergence. This next example has a zero at 0.0, but for most initial values will escape towards ±∞, sometimes causing a relative tolerance to return a misleading value. Here we can see the differences:

julia> f(x) = cbrt(x)*exp(-x^2)
f (generic function with 1 method)

julia> x0 = 0.1147
0.1147

julia> find_zero(f, 1.0, Roots.Order1()) # small relative value of f(xₙ), but not a mathematical zero
5.593607755898642

julia> find_zero(f, 1.0, Roots.Order1(), atol=0.0, rtol=0.0) # error as no check on `|f(xn)|`
ERROR: Roots.ConvergenceFailed("Stopped at: xn = 5.593607755898642. Too many steps taken. ")
[...]

julia> fx = ZeroProblem(f, x0);

julia> solve(fx, Roots.Order1(), atol=0.0, rtol=0.0) # NaN, not an error
NaN

julia> fx = ZeroProblem((f, D(f)), x0) # higher order methods can identify zero of this function
ZeroProblem{Tuple{typeof(f), var"#1#2"{typeof(f)}}, Float64}((f, var"#1#2"{typeof(f)}(f)), 0.1147)

julia> solve(fx, Roots.LithBoonkkampIJzerman(2,1), atol=0.0, rtol=0.0)
0.0

The iterator interface can be useful for hybrid zero-finding algorithms. Here we illustrate the iterator produced by init on a rapidly convergent example:

julia> fx = ZeroProblem(sin, (3,4))
ZeroProblem{typeof(sin), Tuple{Int64, Int64}}(sin, (3, 4))

julia> p = init(fx, Roots.A42())
Roots.A42: x₀: [3.0, 3.5]

julia> 

julia> for _ ∈ p; @show p; end
p = x₁: [3.14156188905231, 3.1416247553172956]

p = x₂: [3.141592653589793, 3.1415926535897936]

julia> p
Converged to x₂: 3.141592653589793

Multiple zeros

The find_zeros function can be used to search for all zeros in a specified interval. The basic algorithm essentially splits the interval into many subintervals. For each, if there is a bracket, a bracketing algorithm is used to identify a zero, otherwise a derivative free method is used to search for zeros. This algorithm can miss zeros for various reasons, so the results should be confirmed by other means.

f(x) = exp(x) - x^4
find_zeros(f, -10, 10)  # -0.815553…,  1.42961…,  8.61317…

(For tougher problems, the IntervalRootFinding package gives guaranteed results, rather than the heuristically identified values returned by find_zeros.)

Convergence

For most algorithms, convergence is decided when

The value |f(x_n)| < tol with tol = max(atol, abs(x_n)*rtol), or
the values x_n ≈ x_{n-1} with tolerances xatol and xrtol and f(x_n) ≈ 0 with a relaxed tolerance based on atol and rtol.

The algorithm stops if

it encounters an NaN or an Inf, or
the number of iterations exceed maxevals, or
the number of function calls exceeds maxfnevals.

If the algorithm stops and the relaxed convergence criteria is met, the suspected zero is returned. Otherwise an error is thrown indicating no convergence. To adjust the tolerances, find_zero accepts keyword arguments atol, rtol, xatol, and xrtol, as seen in some examples above.

The Bisection and Roots.A42 methods are guaranteed to converge even if the tolerances are set to zero, so these are the defaults. Non-zero values for xatol and xrtol can be specified to reduce the number of function calls when lower precision is required.

julia> fx = ZeroProblem(sin, (3,4));

julia> p = init(fx, Bisection(), xatol=1/4)
Bisection: x₀: [3.0, 4.0]

julia> for _ ∈ p; @show p; end
p = x₁: [3.0, 3.5]
p = x₂: [3.0, 3.25]
p = x₃: [3.125, 3.25]

An alternate interface

This functionality is provided by the fzero function, familiar to MATLAB users. Roots also provides this alternative interface:

fzero(f, x0::Real; order=0) calls a derivative-free method. with the order specifying one of Order0, Order1, etc.
fzero(f, a::Real, b::Real) calls the find_zero algorithm with the Bisection method.
fzeros(f, a::Real, b::Real) will call find_zeros.

Usage examples

f(x) = exp(x) - x^4
## bracketing
fzero(f, 8, 9)		          # 8.613169456441398
fzero(f, -10, 0)		      # -0.8155534188089606
fzeros(f, -10, 10)            # -0.815553, 1.42961  and 8.61317

## use a derivative free method
fzero(f, 3)			          # 1.4296118247255558

## use a different order
fzero(sin, big(3), order=16)  # 3.141592653589793...

Technical difference between find_zero and fzero

The fzero function is not identical to find_zero. When a function, f, is passed to find_zero the code is specialized to the function f which means the first use of f will be slower due to compilation, but subsequent uses will be faster. For fzero, the code is not specialized to the function f, so the story is reversed.