I'm wondering whether it is possible to speed up the following code that I'm using to find points on the intersection between a line and the zero locus of an equation.

The idea is the following. First, generate two points $\vec{p}$ and $\vec{q}$ randomly on the 9-dimensional sphere. Each of these points is given as a 5-vector of complex numbers (viewing 10 real coordinates as 5 complex coordinates). Using these two points, you can parametrise a line in 5 complex dimensions as $$\vec{l}(t) = \vec{p} + t\, \vec{q},$$ where $t$ parametrises where on the line you are.

I then want to find the intersection of this line with the equation $$Q(\vec{z}) \equiv z_1^4+\ldots+z_5^4 = 0 ,$$ where $(z_1,\ldots,z_5)$ are again complex coordinates. (In the full problem, $Q(z)$ is actually of arbitrary degree and so I cannot find the roots of $Q=0$ analytically.) The intersection is given by $$Q(\vec{p} + t\, \vec{q})=0,$$ which is then a quartic equation for $t$. There are generically 4 solutions $t_i$ to such an equation. Substituting these values of $t$ back into $\vec{l}(t)$, we find 4 points (4 sets of coordinates $\vec{z}_i=\vec{l}(t_i)$) where the line intersects $Q=0$.

I then repeat this using many randomly generated points $\vec{p}$ and $\vec{q}$ to get a large number of points (usually on the order of 5-10 million).

My problem is that even for 1M points, this takes roughly 90s or so, which is a good chunk of the total runtime of my calculation. At the moment, I have a compiled function that finds the random points on the sphere (by sampling a normal distribution and then scaling the length of the vector to 1), a non-compiled function that does the root finding, and then a final ParallelTable function that repeats this for as many points as I want (and converts the result to a packed array, since I then do some numerical linear algebra with it).

I'd be very interested in any way to speed this up. I come back to this every month or so, and fail at squeezing anymore speed out of it.

I was hoping to get some improvement from compiling the root-finding function as this seems to be the slowest part, but couldn't see a way to do this. I was also wondering if generating all of the random points on the sphere first and then using Listable in some capacity might help, but again I couldn't quite see how to get this to work.

A nagging voice in the back of my head says I should just use C instead, but I've been amazed at how close Mathematica can usually get, so I'm not giving up yet! Thanks for the taking the time to read this!

The code is:

(* define equation we want to solve for Q=0 *)
(* quartic equation so generically 4 roots *)

(* equation of the form z[[1]]^degree+... *)

(* compiled function to generate a random point on a (2*d-1)-dimensional sphere *)
(* express as a complex point in C^d *)
(* generate a 2*d-vector (a point) in R^(2*d) using rotationally symmetric normal distribution *)

(* normalise vector to 1, giving a point on sphere S^(2*d-1) *)

(* convert real 10-vector to complex 5-vector (view as point in C^dim) *)
Part[x,1;;d]+ I Part[x,d+1;;2 d]],

(* Function to generate degree # of points by intersecting the line (p + tq) with Q=0 *)
(* t is variable that we solve for *)
(* define line as (p + tq) where p and q are random points on S^(2*dim - 1) written as complex 5-vectors *)
line=genPoint$S[d]+t genPoint$S[d];

(* solve for t in Q(p+tq)=0 - find degree=4 solutions as Q is quartic *)
(* substitute solutions back into (p + tq) to find points *)
(* get 4 x dim array as output - 4 sets of points, each specified by a complex 5-vector *)

(* function to generate at least N points that lie on Q = 0 *)

(* {0.0004921`,{5}} *)
(* {0.0013839`,{4,5}} *)

(* timing for at least 1000000 points *)
(* {90.7323466`,{1000000,5}} *)

Edit: I've managed to find a ~20% improvement by batching the computation of the points on the sphere. It also produces a packed array by default. The new code is simply:

(*define equation we want to solve for Q=0*)
(*quartic equation so generically 4 roots*)

(*equation of the form z[[1]]^degree+...*)




cx=Part[x, All, 1 ;; d] + I Part[x, All, 1 + d ;; 2 d];
cy=Part[y, All, 1 ;; d] + I Part[y, All, 1 + d ;; 2 d];

lines=cx + t cy;


findPoints$test[dim, degree, 1000000]//Dimensions//AbsoluteTiming
(* {72.7542245`,{1000000,5}} *)
  • $\begingroup$ Does it improve if you set "CompilationTarget"->"C"? $\endgroup$ – Daniel Lichtblau Oct 8 at 20:24
  • 3
    $\begingroup$ Last code takes 9.41695 s on my computer with Parallelize[ , Method -> "CoarsestGrained"] $\endgroup$ – Alex Trounev Oct 13 at 21:20
  • 1
    $\begingroup$ Try to use it as follows findPoints$test[d_, deg_, N_] := Parallelize[(), Method -> "CoarsestGrained"] $\endgroup$ – Alex Trounev Oct 13 at 22:16
  • 2
    $\begingroup$ You said in a comment to the deleted answer that you were particularly interested in a Q that is degree 5 in the z. Of course degree 5 polynomials don't have a solution in radicals. Degree 4 polynomials do, and it is possible to vectorize root finding of degree 4 polynomials, achieving a large speed up. Of course this approach won't work at all for degree 5 polynomials. So, is there any benefit to providing an answer using this appoach? $\endgroup$ – Carl Woll Oct 15 at 0:27
  • 1
    $\begingroup$ Hm. Numerical root finding typically profits from good initial guesses. One could exploit this as follows: Rewrite the random point generation as a Markov chain with decently small increments. Then you can use the solutions of the old point pair as initial guesses for the new point pair and perform a few Newton iterations. If Newton's method converges: Fine. Otherwise, use NRoots as fallback. If the Markov chain is sufficiently long, you still get the product of sphere uniformuly covered. $\endgroup$ – Henrik Schumacher Oct 15 at 16:48

This code devoted for testing to demonstrate effect of parallel job. Let take number of kernels used equal nk, then we can distribute computation as follows

SeedRandom[1234](*we set randomizer for testing only!*); degree = 4;
dim = 5;
LaunchKernels[]; nk = $KernelCount;

Q[z_] := Sum[z[[i]]^degree, {i, 1, dim}];

findPoints$test[d_, deg_, N_, 
   i_] := (x = 
    RandomVariate[NormalDistribution[], {Ceiling[N/deg], 2 d}];
   y = RandomVariate[NormalDistribution[], {Ceiling[N/deg], 2 d}];
   x = x/(Norm /@ x);
   y = y/(Norm /@ y);
   cx = Part[x, All, 1 ;; d] + I Part[x, All, 1 + d ;; 2 d];
   cy = Part[y, All, 1 ;; d] + I Part[y, All, 1 + d ;; 2 d];
   lines = cx + t cy;
   root[i] = 
    Flatten[(# /. {NRoots[Q[#] == 0, t, Method -> "JenkinsTraub"] // 
           ToRules}) & /@ lines, {{1, 2}, {3}}]);

In the case with nk=4 we have

  Table[findPoints$test[dim, degree, 1000000/nk, i] // Dimensions, {i,
     nk}], Method -> "FinestGrained"] // AbsoluteTiming

Out[]= {19.8538, {{250000, 5}, {250000, 5}, {250000, 5}, {250000, 

With other options we have same result, for example,

  Table[findPoints$test[dim, degree, 1000000/nk, i] // Dimensions, {i,
     nk}], Method -> Automatic] // AbsoluteTiming

Out[]= {20.8837, {{250000, 5}, {250000, 5}, {250000, 5}, {250000, 

This result found out with 50% CPU. With 100% CPU it takes 10 s and so on.

| improve this answer | |


Tried an anylytical solution as shown below.

Sorry, but didn't see a programming error. So it turned out that it is much slower. I show it anyway.

Learn, how anylytical solutions look like ...

p = {p1, p2, p3, p4, p5}; q = {q1, q2, q3, q4, q5};

Q[z_] := Total@(z^4)

sol[{p1_, p2_, p3_, p4_, p5_}, {q1_, q2_, q3_, q4_, q5_}] = 
   t /. Solve[Q[p + t q] == 0, t, Quartics -> False]

Solution writen in vector form is

Clear[p, q, p1, p2, p3, p4, p5, q1, q2, q3, q4, q5]

sol4 = {Root[p .p^3 + (4 p^3 .q) #1 + (6 p^2 .q^2) #1^2 + (4  p.q^3) #1^3 + (q .q^3) #1^4 &, 1], 
   Root[p .p^3 + (4 p^3 .q) #1 + (6 p^2 .q^2) #1^2 + (4 p.q^3) #1^3 + (q .q^3) #1^4 &, 2], 
   Root[p .p^3 + (4 p^3 .q) #1 + (6 p^2 .q^2) #1^2 + (4 p.q^3) #1^3 + (q .q^3) #1^4 &, 3], 
   Root[p .p^3 + (4 p^3 .q) #1 + (6 p^2 .q^2) #1^2 + (4 p.q^3) #1^3 + (q .q^3) #1^4 &, 4]

Analysing gives Roots as a Binomial sum of p and q vector.

rsum[d_] := 
 p.p^(d - 1) + 
 Sum[(Binomial[d, j] p^(d - j) . q^j ) #1^j, {j, 1, d - 1}] + 
 q.q^(d - 1) #1^d

sol[d_] := Table[Root[Evaluate[rsum[d]] &, k], {k, 1, d}]

sol[4] == sol4  (*   True   *)

Your function to generate points

genPoint$S = 
 Compile[{{d, _Integer}}, 
 Module[{x},(*generate a 2*d-vector (a point) in R^(2*
d) using rotationally symmetric normal distribution*)
x = RandomVariate[NormalDistribution[], 2 d];
(*normalise vector to 1,giving a point on sphere S^(2*d-1)*)
x = x/Norm[x];
(*convert real 10-vector to complex 5-vector (view as point in  C^
dim)*)Part[x, 1 ;; d] + I Part[x, d + 1 ;; 2 d]], 
"RuntimeOptions" -> "Speed", 
CompilationOptions -> {"InlineExternalDefinitions" -> True}, 
RuntimeAttributes -> Listable, Parallelization -> True]

{pp, qq} = {genPoint$S[5], genPoint$S[5]}

ss[p_, q_] = Evaluate[sol[6]]; 
pp + # qq & /@ ss[pp, qq] // TableForm

Here dimension 6

(ss[p_, q_] = Evaluate[sol[6]]; 
 ParallelTable[({pp, qq} = {genPoint$S[5], genPoint$S[5]};
  pp + # qq & /@ ss[pp, qq]), {10000}];) // AbsoluteTiming
| improve this answer | |

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