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Root finding functions for Julia

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This package contains simple routines for finding roots, or zeros, of scalar functions of a single real variable using floating-point math. The find_zero function provides the primary interface. The basic call is find_zero(f, x0, [M], [p]; kws...) where, typically, f is a function, x0 a starting point or bracketing interval, M is used to adjust the default algorithms used, and p can be used to pass in parameters.

The various algorithms include:

  • Bisection-like algorithms. For functions where a bracketing interval is known (one where f(a) and f(b) have alternate signs), a bracketing method, like Bisection, can be specified. The default is Bisection, for most floating point number types, employed in a manner exploiting floating point storage conventions. For other number types (e.g. BigFloat), an algorithm of Alefeld, Potra, and Shi is used by default. These default methods are guaranteed to converge. Other bracketing methods are available.

  • Several derivative-free algorithms. 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 more function calls to converge, as it employs a bracketing method when possible. The higher order methods promise 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 algorithms that require a derivative or two to be specified: Roots.Newton and Roots.Halley. Roots.Schroder provides a quadratic method, like Newton's method, which is independent of the multiplicity of the zero. This is generalized by Roots.ThukralXB (with X being 2,3,4, or 5).

  • There are several non-exported algorithms, such as, Roots.Brent(), 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

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

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

For non-bracketing methods, the initial position is passed in as a scalar, or, possibly, for secant-like methods an iterable like (x_0, x_1):

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

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

julia> find_zero(f, (3, 2), Order1())  α₁ # start secant method with (3, f(3), (2, f(2))

julia> find_zero(sin, BigFloat(3.0), Order16())  π # 2 iterations to 6 using Order1()

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

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

Newton's method can be used without taking derivatives by hand. The following examples 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

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

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)

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)

The CommonSolve interface

The DifferentialEquations interface of setting up a problem; initializing the problem; then solving the problem is also implemented using the types ZeroProblem and the methods init, solve!, and solve (from CommonSolve).

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

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

julia> find_zero(f, x0, Roots.Order1())  5.075844588445206 # stopped as |f(xₙ)| ≤ |xₙ|ϵ

julia> find_zero(f, x0, 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

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)

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

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

julia> solve(Z, Order1(), 4)  1.2523532340025887  # by position, uses p=4

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 heuristic 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

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

julia> using IntervalSets

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

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


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 find_zero algorithm stops if

  • it encounters an NaN or an Inf, or

  • the number of iterations exceed maxevals

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)

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

julia> fzero(f, -10, 0)  α₀

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

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

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