Addition-chain exponentiation: Difference between revisions

Line 156:

printf("%14d %7d %7d\n", i, seq_len(i), bin_len(i));

return 0;

}</lang>~~output<lang>...~~

}</lang>

output

<pre>...

Exponents:

1 2 4 8 10 18 28 46 92 184 212 424 848 1696 3392 6784 13568 27136 27182

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Line 179:

92 8 9

93 9 10

...</~~lang~~>

...</pre>

=={{header|Go}}==

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count of multiplies: 18

</pre>

=={{header|MATLAB}} / {{header|Octave}}==

⚫

I assume that Matlab and Octave have about such optimization already included. On a single core of an "Pentium(R) Dual-Core CPU E5200 @ 2.50GHz", the computation of 100000 repetitions of the matrix exponentiation A^27182 and A^31415 take about 2 and 2.2 seconds, resp.

⚫

I assume that Matlab and Octave have about such optimization already included. On a single core of an "Pentium(R) Dual-Core CPU E5200 @ 2.50GHz", the computation of 100000 repetitions of the matrix exponentiation A^27182 and A^31415 take about 2 and 2.2 seconds, resp.

<lang Matlab>x = sqrt(.5);

A = [x,0,x,0,0,0;0,x,0,x,0,0; 0,x,0,-x,0,0;-x,0,x,0,0,0;0,0,0,0,0,1; 0,0,0,0,1,0];A

t = cputime(); for k=1:100000,x1=A^27182;end; cputime()-t,x1,

t = cputime(); for k=1:100000,x2=A^31415;end; cputime()-t,x2, </lang>

Output:

<pre>Octave3.4.3> t = cputime(); for k=1:100000,x1=A^27182;end; cputime()-t,x1,