# How to find numerically all roots of a function in a given range?

It is common that I search numerically for all zeros (roots) of a function in a given range. I have written two simple minded functions that perform this task, and I know of similar functions on this site (e.g. this, this, and this).

I think this community will benefit if we could compile a list of functions that do so, with some explanations about efficiency considerations, in what context should we use which approach, etc.

The problem definition: given a function f and a range {x1,x2}, write a function that finds all (or most) roots of f in the given range.

• The answer depends on how f is represented. Is it a pure black box, or is it possible to do exact operations on it (to, as a random example, factor out found roots). Can we take its derivatives? Can we assume it is defined outside of Interval[{x1,x2}], perhaps out onto some open set of the Complexes? – Eric Towers Aug 18 '15 at 3:23
• @EricTowers Good questions. The purpose of this post is to compile a list of answers that will suit different situations. If you have a solution that applies only to a few cases, please post it. – yohbs Aug 18 '15 at 9:29
• Er... have you guys ever seen this? library.wolfram.com/infocenter/Demos/4482 (Back in the days, Ted Ersek's RootSearch was all the rage on MathGroup) - Yeah, I know, it will come up it has been used below somewhere and I did not pay enough attention...) – Peltio Aug 21 '15 at 8:04
• @Peltio, yeah I remember that… now I'm wondering which of RootSearch[] or Wagon's FindAllCrossings[] came first. – J. M. is away Aug 21 '15 at 13:54
• @J.M. I wonder whether I should accept one of the answers or not. There's really no "correct answer" here, and this kind of a community wiki question. – yohbs Aug 27 '15 at 7:27

First, it might be worth pointing out that in recent versions of Mathematica, Solve and NSolve are quite strong at solving equations with standard special functions.

With[{f = BesselJ[1, #^(3/2)] Sin[#] &},
solvesol = x /. Solve[{f[x] == 0, 25 <= x <= 35}, x];
Plot[f[x], {x, 25, 35},
MeshFunctions -> {# &}, Mesh -> {solvesol},
MeshStyle -> Directive[PointSize[Medium], Red]
]
]


Solve::nint: Warning: Solve used numeric integration to show that the solution set found is complete. >>

For other functions, provided they are continuous and not too oscillatory, then in addition to ODE approach in yohbs's NDSolve solution, we can solve the system with a DAE that does not need the function to be differentiable.

ClearAll[NrootSearch2];

Options[NrootSearch2] = Options[NDSolve];
NrootSearch2[f_, x1_, x2_, opts : OptionsPattern[]] :=
Module[{x, y, t, s},
Reap[
NDSolve[{x'[t] == 1, x[x1] == x1, y[t] == f[t],
WhenEvent[y[t] == 0, Sow[s /. FindRoot[f[s], {s, t}],
"zero"],
"LocationMethod" -> "LinearInterpolation"]},
{}, {t, x1, x2}, opts],
"zero"][[2, 1]]];

With[{f = BesselJ[1, #^(3/2)] Sin[#] &},
nrootsol = NrootSearch2[f, 25, 35];
Plot[f[x], {x, 25, 35},
MeshFunctions -> {# &}, Mesh -> {nrootsol},
MeshStyle -> Directive[PointSize[Medium], Red]
]
]


For functions like the example we've been using, we can combine the previous method with Root to produce exact results. (Caveat: Managing the precision of the approximate root is not always straightforward. Adjusting the WorkingPrecision option to FindRoot might be necessary. The code below tries it first at $MachinePrecision, and if that fails, then it tries a WorkingPrecision of 40.) ClearAll[rootSearch2]; Options[rootSearch2] = Options[NDSolve]; rootSearch2[f_, x1_, x2_, opts : OptionsPattern[]] := Module[{x, y, t, s, res, tmp}, Reap[ NDSolve[{x'[t] == 1, x[x1] == x1, y[t] == f[t], WhenEvent[y[t] == 0, Sow[Quiet[ res = Check[ Root[{f[#] &, s /. FindRoot[f[s], {s, t}, WorkingPrecision ->$MachinePrecision]}],
$Failed]]; If[res ===$Failed,  (* if $MachinePrecision fails, try a higher one *) Quiet[ res = Check[ Root[{f[#] &, tmp = s /. FindRoot[f[s], {s, t}, WorkingPrecision -> 40]}], res = tmp]]]; (* if both fail, return approximate root *) res, "zero"], "LocationMethod" -> "LinearInterpolation"]}, {}, {t, x1, x2}, opts], "zero"][[2, 1]]];  Note it returns 8 π etc. for the roots of the sine factor: With[{f = BesselJ[1, #^(3/2)] Sin[#] &}, exactsol = rootSearch2[f, 25, 35] ] (* {8 π, Root[{BesselJ[1, #1^(3/2)] Sin[#1] &, 25.192448602298225837336093255176323600186894730 + 0.*10^-46 I}], Root[{BesselJ[1, #1^(3/2)] Sin[#1] &, 25.60802500579825}], ..., 11 π, Root[{BesselJ[1, #1^(3/2)] Sin[#1] &, 34.76570243333289}]} *)  Comparisons: The two exact methods: SortBy[N]@solvesol - exactsol // N[#,$MachinePrecision] &


N::meprec: Internal precision limit $MaxExtraPrecision = 50. reached while evaluating {0,<<28>>,0}. >> (* {0, 0.*10^-65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0} *)  The two root-search methods: nrootsol - N@exactsol Max@Abs[%] (* {0., 4.79616*10^-13, 3.55271*10^-15, 0., 0., 0., 0., 0., 0., -1.84741*10^-13, 2.8777*10^-13, 0., 0., 0., 0., 0., -3.55271*10^-15, 0., 0., 3.01981*10^-13, 0., 0., 0., 0., 0., 0., 7.10543*10^-15, -5.96856*10^-13, 7.10543*10^-15, 0.} 5.96856*10^-13 *)  One approach I've started to become fond of, apart from Plot[]-based approaches, involves the Chebyshev expansion of a function, followed by the construction of the corresponding "colleague matrix" (a matrix whose characteristic polynomial is the Chebyshev series previously determined), and then the computation of the colleague matrix's eigenvalues, which are hopefully good root approximations (perhaps followed by a polishing with FindRoot[] if wanted). The method is discussed in more detail in Boyd's book. Using yohbs's example: f = BesselJ[1, #^(3/2)] Sin[#] &; {xmin, xmax} = {25, 35}; n = 64; cnodes = Rescale[N[Cos[Pi Range[0, n]/n], 20], {-1, 1}, {xmin, xmax}]; cc = Sqrt[2/n] FourierDCT[f /@ cnodes, 1]; cc[[{1, -1}]] /= 2; colleague = SparseArray[{{i_, j_} /; i + 1 == j :> 1/2, {i_, j_} /; i == j + 1 :> 1/(2 - Boole[j == 1])}, {n, n}] - SparseArray[{{i_, n} :> cc[[i]]/(2 cc[[n + 1]])}, {n, n}]; rts = Sort[Select[DeleteCases[ Rescale[Eigenvalues[colleague], {-1, 1}, {xmin, xmax}], _Complex | _DirectedInfinity], xmin <= # <= xmax &]]; Plot[f[x], {x, xmin, xmax}, Epilog -> {Directive[Red, PointSize[Medium]], Point[Transpose[PadRight[{rts}, {2, Automatic}]]]}]  A more sophisticated approach which automatically chooses the number of sample points (in the style of Clenshaw-Curtis quadrature) is used in the MATLAB package Chebfun; as it is a bit more elaborate, I haven't gotten around to implementing it. Maybe one of these days... • Very interesting! You got a mix-up in the naming: comrade should be colleague in the expression for rts. Actually, maybe one should call it companion. – Jens Aug 21 '15 at 5:46 • @Jens, whoops yes. Mixed it up. Anyway: "companion" is quite general, "Frobenius companion" is the customary, well-known one, "comrade" refers to an orthogonal polynomial basis, and "colleague" is a Chebyshev comrade matrix. I felt out of breath just typing that… :D – J. M. is away Aug 21 '15 at 5:54 • J.M Would you tell me which font(Namely, the font of ticks 0.06, 0.04, 0.02,..) do you used in your graphic? – xyz Aug 21 '15 at 6:07 • @ShutaoTang Try adding the options ,PlotTheme->"Classic",Frame->True to Plot. – Jens Aug 21 '15 at 6:09 • I should probably stress this: compared to the functionality of MATLAB's chebfun, this is very primitive. It might take me a while to come up with working code for automatically choosing n. – J. M. is away Aug 22 '15 at 6:56 The first approach is to evaluate the function at equidistant points, and look for sign changes. The distance between two sampled points, dx, is an input to the function. When a sign change happens, use FindRoot, which is constrained to look for the root only between the two points that encompass the sign change. The function accepts all the Options that FindRoot accepts. rootSearch[f_, x1_, x2_, dx_, ops : OptionsPattern[]] := Block[{xs, fs, fsb, pos}, xs = Range[x1, x2, dx]; fs = f /@ xs; fsb = Thread[fs > 0]; pos = Flatten@Position[Thread[Xor[fsb // Rest, fsb // Most]], True]; x /. ParallelTable[ Quiet@ FindRoot[ f[x], {x, (xs[[p]] + xs[[p + 1]])/2, xs[[p]], xs[[p + 1]]}, ops], {p, pos}] ]; Options[rootSearch] = Options[FindRoot]  This approach relies on knowing the adequate dx in advance. Setting it too low might result in slower computation, setting it too high might result in missing some roots. The second approach does not have this caveat. It uses the built-in adaptive-mesh algorithms of NDSolve. The idea is to solve a differential equation that follows f, and look for sign changes. The function, rootSearchD, accepts all the Options of NDSolve. rootSearchD[f_, x1_, x2_, ops : OptionsPattern[]] := Block[{fp, f1}, fp = Derivative[1][f]; f1 = f[x1]; Last[Last[Reap[ NDSolve[{y'[x$] == fp[x$], y[x1] == f1, WhenEvent[y[x$] == 0, Sow[x$]]}, y, {x$, x1, x2}, ops]
]]]
];
Options[rootSearchD] = Options[NDSolve];


A simple verification:

rootSearchD[Sin[#] &, 10, 20]/Pi
rootSearch[Sin[#] &, 10, 20, 1]/Pi

(*Output: {4., 5., 6.}*)


A more challenging example:

SetOptions[Plot, ImageSize -> 300, Axes -> {True, False}];
With[{f = BesselJ[1, #^(3/2) ] Sin[#] &},
x1 = rootSearch[f, 25, 35, 0.1];
x2 = rootSearchD[f, 25, 35];
GraphicsRow@{
Plot[f[x], {x, 25, 35},
Epilog -> {Red, PointSize[Medium], Point[Transpose[{x1, 0 x1}]]}],
Plot[f[x], {x, 25, 35},
Epilog -> {Red, PointSize[Medium], Point[Transpose[{x2, 0 x2}]}]}
]


It is seen that rootSearch (the left panel) misses two roots that rootSearchD (the right panel) catches.

• The alternative to equispaced sampling, as done in the first routine, is to use Plot[]` for adaptive sampling; some of the routines linked to in the OP do this. – J. M. is away Aug 17 '15 at 22:59