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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_zero function 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:

julia> using Roots

julia> f(x) = exp(x) - x^4;

julia> α₀,α₁,α₂ = -0.8155534188089607, 1.4296118247255556, 8.6131694564414;

julia> find_zero(f, (8,9), Bisection()) ≈ α₂ # a bisection method has the bracket specified
true

julia> find_zero(f, (-10, 0)) ≈ α₀ # Bisection is default if x in `find_zero(f,x)` is not a number
true


julia> find_zero(f, (-10, 0), Roots.A42()) ≈ α₀ # fewer function evaluations
true

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

julia> find_zero(f, 3) ≈ α₁  # find_zero(f, x0::Number) will use Order0()
true

julia> find_zero(f, 3, Order1()) ≈ α₁ # same answer, different method (secant)
true

julia> find_zero(sin, BigFloat(3.0), Order16()) ≈ π
true

The find_zero function can be used with callable objects:

julia> using Polynomials;

julia> x = variable();

julia> find_zero(x^5 - x - 1, 1.0) ≈ 1.1673039782614187
true

The function should respect the units of the Unitful package:

julia> using Unitful

julia> s, m  = u"s", u"m";

julia> g, v₀, y₀ = 9.8*m/s^2, 10m/s, 16m;


julia> y(t) = -g*t^2 + v₀*t + y₀
y (generic function with 1 method)

julia> find_zero(y, 1s)  ≈ 1.886053370668014s
true

Newton's method can be used without taking derivatives by hand. The following use the ForwardDiff package:

julia> using ForwardDiff

julia> D(f) = x -> ForwardDiff.derivative(f,float(x))
D (generic function with 1 method)

Now we have:

julia> f(x) = x^3 - 2x - 5
f (generic function with 1 method)

julia> x0 = 2
2

julia> find_zero((f,D(f)), x0, Roots.Newton()) ≈ 2.0945514815423265
true

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

julia> using Statistics: mean, median

julia> as = rand(5);

julia> M(x) = sum([(x-a)^2 for a in as])
M (generic function with 1 method)

julia> find_zero(D(M), .5) ≈ mean(as)
true

julia> med(x) = sum([abs(x-a) for a in as])
med (generic function with 1 method)

julia> find_zero(D(med), (0, 1)) ≈ median(as)
true

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
true

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
true

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()) # stopped as |f(xₙ)| ≤ |xₙ|ϵ
5.53043654482315

julia> find_zero(f, 1.0, Roots.Order1(), atol=0.0, rtol=0.0) # error as no check on `|f(xn)|`
ERROR: Roots.ConvergenceFailed("Algorithm failed to converge")
[...]

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

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

Functions may be parameterized, as illustrated:

julia> f(x, p=2) = cos(x) - x/p
f (generic function with 2 methods)

julia> Z = ZeroProblem(f, pi/4)
ZeroProblem{typeof(f), Float64}(f, 0.7853981633974483)

julia> solve(Z, Order1()) ≈ 1.0298665293222586     # use p=2 default
true

julia> solve(Z, Order1(), p=3) ≈ 1.170120950002626 # use p=3
true

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.

julia> f(x) = exp(x) - x^4
f (generic function with 2 methods)

julia> find_zeros(f, -10,10) ≈ [α₀,α₁,α₂] # from above
true

The interval can also be specified using a structure with extrema defined, where extrema return two different values:

julia> using IntervalSets

julia> find_zeros(f, -10..10) ≈ [α₀,α₁,α₂]
true

(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

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> solve(fx, Bisection(); xatol=1/16)
3.125

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

julia> f(x) = exp(x) - x^4
f (generic function with 2 methods)

julia> fzero(f, 8, 9) ≈ α₂   # bracketing
true

julia> fzero(f, -10, 0) ≈ α₀
true

julia> fzeros(f, -10, 10) ≈ [α₀, α₁, α₂]
true

julia> fzero(f, 3) ≈ α₁      # default is Order0()
true

julia> fzero(sin, big(3), order=16)  ≈ π # uses higher order method
true