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Dear friends

It seems that Maple takes a long time to evaluate the square roots of numbers.

See the simple code below.

st := time();

for i to 1000 do for j to 1000 do

a[i, j] := evalf(abs(i-j+1)^0.3-abs(i-j)^0.3):

end: end:


I run it, then after a few seconds I run it again and again  to see the consuming time: once the running time is 77 seconds, then is 57 seconds, again is 73 seconds ...

Two questions:

1- Why the time is so differnt?

2- Why a simple code is being done at about a minute? Based on the number of operations, I think it should be done at less than a second. It just involves finding two million real third roots each of them less than 100 operations (if Newton method for finding roots is applied it probably needs less than 20 operations). I was thinking that a computer may do one billion operations per second. 

Since I need to report my numerical results in a scientific paper, it is important for me to know what's going on.

It is worthy of noting that I use Maple 18 on a Lenovo Laptop with Corei3 1.90 GHz with 64 bit operating system and 4 Gb RAM.

In advance, I appreciate for helping me to reveal the secrets.

Thank you all


I thought I would share some code for computing sparse matrix products in Maple.  For floating point matrices this is done quickly, but for algebraic datatypes there is a performance problem with the builtin routines, as noted in

Download spm.txt

The code is fairly straightforward in that it uses op(1,A) to extract the dimensions and op(2,A) to extract the non-zero elements of a Matrix or Vector, and then loops over those elements.  I included a sparse map function for cases where you want to map a function (like expand) over non-zero elements only.

# sparse matrix vector product
spmv := proc(A::Matrix,V::Vector)
local m,n,Ae,Ve,Vi,R,e;
n, m := op(1,A);
if op(1,V) <> m then error "incompatible dimensions"; end if;
Ae := op(2,A);
Ve := op(2,V);
Vi := map2(op,1,Ve);
R := Vector(n, storage=sparse);
for e in Ae do
n, m := op(1,e);
if member(m, Vi) then R[n] := R[n] + A[n,m]*V[m]; end if;
end do;
return R;
end proc:
# sparse matrix product
spmm := proc(A::Matrix, B::Matrix)
local m,n,Ae,Be,Bi,R,l,e,i;
n, m := op(1,A);
i, l := op(1,B);
if i <> m then error "incompatible dimensions"; end if;
Ae := op(2,A);
Be := op(2,B);
R := Matrix(n,l,storage=sparse);
for i from 1 to l do
Bi, Be := selectremove(type, Be, (anything,i)=anything);
Bi := map2(op,[1,1],Bi);
for e in Ae do
n, m := op(1,e);
if member(m, Bi) then R[n,i] := R[n,i] + A[n,m]*B[m,i]; end if;
end do;
end do;
return R;
end proc:
# sparse map
smap := proc(f, A::{Matrix,Vector})
local B, Ae, e;
if A::Vector then
B := Vector(op(1,A),storage=sparse):
B := Matrix(op(1,A),storage=sparse):
end if;
Ae := op(2,A);
for e in Ae do
B[op(1,e)] := f(op(2,e),args[3..nargs]);
end do;
return B;
end proc:

As for how it performs, here is a demo inspired by the original post.

n := 674;
k := 6;
A := Matrix(n,n,storage=sparse):
for i to n do
  for j to k do
    A[i,irem(rand(),n)+1] := randpoly(x):
  end do:
end do:
V := Vector(n):
for i to k do
  V[irem(rand(),n)+1] := randpoly(x):
end do:
C := CodeTools:-Usage( spmv(A,V) ):  # 7ms, 25x faster
CodeTools:-Usage( A.V ):  # 174 ms
B := Matrix(n,n,storage=sparse):
for i to n do
  for j to k do
    B[i,irem(rand(),n)+1] := randpoly(x):
  end do:
end do:
C := CodeTools:-Usage( spmm(A,B) ):  # 2.74 sec, 50x faster
CodeTools:-Usage( A.B ):  # 2.44 min
# expand and collect like terms
C := CodeTools:-Usage( smap(expand, C) ):

Leading on from this post I made before:

I have a script that can now integrate larger sums thanks to the tips I was given. I now have a question regarding cpu usage when the calculation is run through a loop. I have attached two files. One contains the sum (large_sum.txt) which is then read in to the main Maple script (

When the loop is run, the first few points are calculated quite consistently with very similar cpu times. However, as the calculation progresses the cpu time suddenly increases (with larger sums than the one given it is a very severe increase) it then decreases again and returns to the time it took for the initial points to be calculated.

Is there a reason there is a sticking point in this calculation? is there a more efficient way to simplify it before it reaches the integration stage? When using simplify(...,size) the initial block takes a very long time to execute hence is not included here. This is not the largest sum that needs to be processed so I am looking for means to speed up calculation time/make it more consistent.

Any help is appreciated



Hi, Im now trying to run my code. But it took like years to even getting the results. may I know any solutions on how to get faster results? Because I have run this code for almost 4 hours yet there is still 'Evaluating...' at the corner left. And when I tried to stop the program, it will stop at 'R1...'.


Digits := 18;
with(plots):n:=1.4: mu(eta):=(diff(U(eta),eta)^(2)+diff(V(eta),eta)^(2))^((n-1)/(2)):
Eqn1 := 2*U(eta)+(1-n)*eta*(diff(U(eta), eta))/(n+1)+diff(W(eta), eta) = 0;
Eqn2 := U(eta)^2-(V(eta)+1)^2+(W(eta)+(1-n)*eta*U(eta)/(n+1))*(diff(U(eta), eta))-mu(eta)*(diff(U(eta), eta, eta))-(diff(U(eta), eta))*(diff(mu(eta), eta)) = 0;
Eqn3 := 2*U(eta)*(V(eta)+1)+(W(eta)+(1-n)*eta*U(eta)/(n+1))*(diff(V(eta), eta))-mu(eta)*(diff(V(eta), eta, eta))-(diff(V(eta), eta))*(diff(mu(eta), eta)) = 0;
bcs1 := U(0) = 0, V(0) = 0, W(0) = 0;
bcs2 := U(4) = 0, V(4) = -1;
R1 := dsolve({Eqn1, Eqn2, Eqn3, bcs1, bcs2}, {U(eta), V(eta), W(eta)}, initmesh = 30000, output = listprocedure, numeric);
Warning, computation interrupted
for l from 0 by 2 to 4 do R1(l) end do;
plot1 := odeplot(R1, [eta, U(eta)], 0 .. 4, numpoints = 2000, color = red);


Thankyou in advance :)

There are dozens of indefinite integral expressions in my worksheet. Everytime I execute the entire worksheet, the cursor always rests beside one indefinite integral expreesion and Maple stays in "Evaluating...". Even 30 minutes passed, the result of the integral couldn't come out. What bothers me is that the cursor would rest beside different indefinite integral expressions. For example, I write 4 indefinite integral expressions A, B, C and D one by one. First time, the results of A, B and C come out and the cursor rests beside D with "Evaluating...". Next time, the results of A and B come out and the cursor rests beside C with "Evaluating...".

Before the indefinite integral expressions, I wrote dozens of lines of codes aiming at assigning values to variables. As I typed more and more indefinite integral expressions into the worksheet, even evaluating the codes aiming at assigning values would spend more and more time.

Does Maple evaluate the codes line by line from the top to the end of a worksheet? If it is true, why evaluating the codes before the indefinite integral expressions becomes slowly?

How to evaluate the entire worksheet without stuck in one indefinite integral expression?


I was wondering why there is difference in computer speed between two almost the same calculations.


61! + 1 costs 271 sec and 59! + 1 almost 0? Why is that? When there are small prime factors, the

calculation goes often faster, why? hanks in advance!


When I run a maple file it uses 25% of CPU.

When I run 2 files, the half of CPU is used.

How can I change the preferences for using most of CPU

I am trying to use Maple 18 to do some computations with matrices over a ring of polynomials in one variable over the integers $\mathbb{Z}[x]$, or the corresponding field of fractions $\mathbb{Q}(x)$.


The matrices in question are of dimension approximately 5000 and are sparse. The algorithm requires at least as many matrix multiplications as the dimension of the space.

Doing some small examples, of dimension 674, with a laptop (i7-3520 M CPU @2.9GHz with 8GB of Ram) gave the following disappointing result:




When a colleague with access to a Mathematica license performed an identical calculation using sparse matrices in Mathematica, we found that Mathematica performed the calcuation in fractions of a second.


In small dimensional examples, constructing the matrices over the field of fractions as sparse in Maple 18 resulted in a four fold decrease in the already disappointing performance of the LinearAlgebra package in Maple 18.


Is there any way to improve the computational performance of Maple 18 for symbolic linear algebra? Alternatively, is the performance of Maple 2015 for symbolic linear algebra noticably better than Maple 18?


Thanks in advance.




My 5 year old laptop executes some commands very slowly e.g. plottools(rotate) around a line which is not a Cartesian axis. I intend to buy a new desktop. Which off-the-shelf desktops provide the fastest processing of Maple commands which do not include large data sets? Which provide the best price/performance?

Hi everyone,

Consider the following worksheet (Maple 13):

 (or as a Google Drive link)

Choose execute entire worksheet on both Maple 13 and Maple 18.

Maple 13 is blindingly fast at 40 seconds, entire wroksheet upon completion

Maple 18 crawls and hangs forever.


Can anyone recognise what the problem with Maple 18 is?

Running on a two core 1.7GHz AMD Athlon.


Many thanks



I have an indexed equation that contains serval definite integrals in it. I want maple to evaluate the equation for different indices. But when I set the parameter "N=100" in the code, it takes maple lots of time for the evaluation. I am looking for some tricks to make the code numerically more efficient. I will be so thankful for any opinion and help.
you can find my code below. The code is so simple and just contains few lines. I will appreciate any help.

Thanks in advance.

I have a long and complicated expression (say Eq1). I have to solve the two equations (obtained by taking real and imaginary parts of Eq1) for two unknowns. Normally it take around a day on my i5 (3.1 GHz, 4 cores), 8gb ram desktop. I looked into posts related to parallel programming but couldn't get much to reduce the computation time. Is there a way to reduce the computation time ?
Many thanks in advance.

I made a small change to the Task Filtering code I uploaded a few weeks ago.  The new code has better memory performance and, most importantly has more stable memory usage which means it can actually run very large examples.  Here is the new version of the code:

FilterCombPara := module( )
    local ModuleApply,

    lessThanX := proc( x, i ) x<=i; end;

    doSplit := proc( i::integer, prefix::set(posint), rest::set(posint),
                                                k::posint, filter::appliable, $ )
        splitCombs( prefix union {i}, remove( lessThanX, rest, i ), k-1, filter );

    splitCombs := proc( prefix::set(posint), rest::set(posint), k::posint,
                                                                filter::appliable, $ )
        if ( numelems( rest ) < k ) then
        elif ( k = 1 ) then
            filterCombs( prefix, rest, filter );
            op( Threads:-Map( doSplit, rest, prefix, rest, k, filter ) );

    makeNewPrefix := proc( i, prefix ) { i, op( prefix ) } end;
    filterCombs := proc( prefix::set(posint), rest::set(posint), filter::appliable, $ )
        local i, f;

        op(select( filter, map( makeNewPrefix, rest, prefix ) )):

    ModuleApply := proc( n::posint, k::posint, filter::appliable, $ )
        [ splitCombs( {}, {seq( i,i=1..n )}, k, filter ) ];


This code has the small mapping functions as module members instead of declared inline.  This means that less memory is churned as this code is excuted.  For a long runs, this helps keeps the memory stable.

As an example, I ran the following example:

> CodeTools:-Usage( FilterCombPara( 113,7, x->false ) );
memory used=17.39TiB, alloc change=460.02MiB, cpu time=88.52h, real time=20.15h

It used 88 CPU hours to run, but only 20 hours of real time (go parallelism!)  It used 17 Terabytes of memory, but only allocated 500 M.  This example is pretty trival, as the filter returned false for all combinations, so it did not collect any matches during the run.  However as long as the number of matches is small, that shouldn't be an issue.  If the number of matches is too large to fit in memory, then this code may need to be modified to write the matches out to disk instead of trying to hold them all in memory at once.


-- Kernel Developer Maplesoft

As described on the help page ?updates,Maple17,Performance, Maple 17 uses a new data structure for polynomials with integer coefficients. Our goal was to improve the performance and parallel speedup of polynomial algorithms that underpin much of the system and create a platform for large scale polynomial computations. Shown below is the new representation for 9xy3z

Dear Maple users

I have had Maple creating graphics for me that I cannot do in other programs I have access to: 3D pictures of circle waves interferring or even the result of an interference pattern from a diffraction grating in Physics. But when it comes to simple animations, I am not all that impressed.

Basicly I have three complaints: 

a)  Maple seems to use a lot of space to save frame information, resulting in large filesizes.

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