Is there a built-in function to do binary search? Say, given a list (sorted) and a number, find the position which keeps the listed sorted when the number is inserted.

I know that LengthWhile could manage that, but it's slow.

  • $\begingroup$ This might be relevant. See also Mr. W's answer there. $\endgroup$
    – Szabolcs
    Jan 19, 2015 at 16:10

3 Answers 3


There is some built-in binary search code but not in the core language as far as I know.

  • There is BinarySearch from the Combinatorica package, which is still the function I use most often despite the fact that that package is now deprecated and loading it causes shadowing of some Symbols.

  • There is the undocumented GeometricFunctions`BinarySearch but this function does not appear to perform particularly well.

When I need greater performance I typically use a compiled form of Leonid's code from:


It seems that since 2021 there is a ResourceFunction that does this.


ResourceFunction["BinarySearch"][{1, 2, 5, 5, 7, 12}, 6]

I looked at the definition of the resource function: it seems to mainly add error handling to GeometricFunctions`BinarySearch which, according to @Mr.Wizard's answer, "does not appear to perform particularly well."

The documentation for the resource function mentions under "Properties and Relations" that:

BinarySearch can be considerably faster for packed arrays

with the example

packed = Sort[RandomReal[{0, 100}, 100000]];
RepeatedTiming[ResourceFunction["BinarySearch"][packed, 50]]

Here are some comparisons:

AbsoluteTiming[LengthWhile[packed, #<50&]] 

{0.016084, 50113}

bsearchResource = ResourceFunction["BinarySearch"]
AbsoluteTiming[bsearchResource[packed, 50]]

(* {0.000474, 50113} *)

Here's BinarySearch from the Combinatorica package mentioned in Mr.Wizard's answer. The package is deprecated and the user has to load the package beforehand with Needs["Combinatorica`"]:


{0.000183, 100227/2}

Then the bsearch function in the link by Mr.Wizard. This is the version that is not compiled:

AbsoluteTiming[bsearchMinNoCompile[packed, 50]]

{0.000159, 50113}

The C compiled version of bsearch (I changed the original complex type to real):

AbsoluteTiming[bsearchMinCompile[packed, 50]]

{0.000042, 50113}

Now looking at the difference between bsearchMinNoCompile and the resource function by increasing the length of packed by a factor of 100:

packed = Sort[RandomReal[{0, 100}, 10000000]];

RepeatedTiming[bsearchMinNoCompile[packed, 50]]

{0.0000492547, 4998443}

RepeatedTiming[bsearchResource[packed, 50]]

{0.0000291153, 4998443}

Summary: At least from the example provided by the resource function, it seems that ResourceFunction["BinarySearch"] provides a convenient method to obtain results quickly when the lists are sorted. The function also has some error handling.


There are two relevant functions at Wolfram Function Repository (WFR) submitted by "Wolfram Staff":

Of course, one can see or follow the examples in the WFR pages. Nevertheless, here are examples that demonstrate the speed of BinarySearch.

packed = Sort[RandomReal[{0, 100}, 100000]];

s = packed[[1332]]

(* 1.35978 *)

ResourceFunction["BinarySearch"][packed, s]

(* 1332 *)

Timings comparison:

 Do[ResourceFunction["BinarySearch"][packed, s], 1000]]

(* {0.194593, Null} *)

AbsoluteTiming[Do[Position[packed, s], 1000]]

(* {4.6829, Null} *)
  • 1
    $\begingroup$ For a packed array of up to 140-150K elements, a full linear search Pick[Range@Length@packed, Unitize[packed - s], 0] is just as fast or faster. And finds duplicates. (+1) $\endgroup$
    – Michael E2
    Sep 14, 2022 at 11:41
  • $\begingroup$ @MichaelE2 Interesting and good to know -- thanks! (You can write to WFR staff suggesting to enhance BinarySearch with that code.) $\endgroup$ Sep 14, 2022 at 11:43

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.