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Re: Re: Working with factors of triangular numbers.
*To*: mathgroup at smc.vnet.net
*Subject*: [mg80042] Re: [mg79864] Re: Working with factors of triangular numbers.
*From*: "Oleksandr Pavlyk" <pavlyk at gmail.com>
*Date*: Sat, 11 Aug 2007 02:09:36 -0400 (EDT)
*References*: <200707030923.FAA17995@smc.vnet.net>
By design factorCounts[n] should give the same as
Sort[FactorInteger[ (n(n+1)/2) - 1][[All,2]]]
Here are few cases where they disagree:
In[134]:= Cases[
Table[{chi, FactorInteger[chi*(chi + 1)/2 - 1][[All, 2]] // Sort,
factorCounts[chi]}, {chi, 100}], {chi_, x_, y_} /; x =!= y]
Out[134]= \
{{13,{1,1,2},{1,1}},{22,{1,2,2},{1,2}},{31,{1,1,2},{1,1}},{40,{1,1,2},\
{1,1}},{49,{1,2,3},{1,3}},{58,{1,1,1,2},{1,1,1}},{67,{1,1,2},{1,1}},{\
76,{1,2,2},{1,2}},{85,{1,1,1,2},{1,1,1}},{94,{1,2,4},{1,4}}}
Oleksandr
On 8/10/07, Oleksandr Pavlyk <pavlyk at gmail.com> wrote:
> Hi,
>
> First of all, very nice work.
>
> It threw me off initially that lld5 did not work for my example:
>
> In[70]:= lld5[chi = 2^84*5^18*7^6]
>
> Out[70]= 14
>
> In[71]:= lldaux5[FactorInteger[chi][[All, 2]] ]
>
> Out[71]= 26
>
> But then I realize that factorCounts is supposed to work only for
> triangular numbers,
> as is implied by the link given in the mail, and I would like to
> explicitly state for the record:
>
> In[72]:= factorCounts[chi]
>
> Out[72]= {1,1,1,1,1,1,1,1,1,1,1,1,5}
>
> In[73]:= FactorInteger[chi]
>
> Out[73]= {{2,84},{5,18},{7,6}}
>
> Oleksandr Pavlyk
>
> On 8/10/07, DrMajorBob <drmajorbob at bigfoot.com> wrote:
> > Here's a significantly faster code (for n=12,13 anyway).
> >
> > Clear[lldaux5, lld5, t5, factorCounts]
> > factorCounts[1] = factorCounts[2] = factorCounts[3] = {1};
> > factorCounts[4] = {2};
> > factorCounts[7] = {3};
> > factorCounts[k_Integer] :=
> > If[EvenQ@k, factorCounts[(k + 2)/2, k - 1],
> > factorCounts[(k - 1)/2, k + 2]]
> > factorCounts[j_Integer, k_Integer] :=
> > Switch[GCD[j, k],
> > 1,
> > Join[FactorInteger@j, FactorInteger@k],
> > 3, Replace[
> > Join[FactorInteger[j/3],
> > FactorInteger[k/3]], {3, s_} :> {3, s + 2}, {1}]][[All, -1]] //
> > Sort
> > lldaux5[{}] := 0
> > lldaux5[x_List] /; MemberQ[x, 1] :=
> > Count[x, 1] + lldaux5@DeleteCases[x, 1]
> > lldaux5[Lpow_] :=
> > lldaux5[Lpow] =
> > With[{Mtup = Tuples[Range[0, #] & /@ Lpow]},
> > Total[Quiet@
> > LinearProgramming[ConstantArray[-1, Length[Mtup]],
> > Transpose@Mtup, Thread@{Lpow, 0},
> > Array[{0, 1} &, Length[Mtup]], Integers]] - 1]
> > lld5[n_Integer] := lldaux5@factorCounts@n
> > t5[n_] := # (# + 1)/2 &@NestWhile[# + 1 &, 2, lld5[#] < n &]
> >
> > T2c /@ Range[10] // Timing
> > t5 /@ Range[10] // Timing
> >
> > {0.422, {3, 15, 55, 253, 1081, 13861, 115921, 665281, 18280081,
> > 75479041}}
> >
> > {1.046, {3, 15, 55, 325, 1081, 18145, 226801, 665281, 18280081,
> > 75479041}}
> >
> > T2c[11] // Timing
> > t5[11] // Timing
> >
> > {1.625, 2080995841}
> >
> > {3.063, 2080995841}
> >
> > Timing differences aren't reliable either way for n<=11, but beyond that
> > it's a different story:
> >
> > T2c[12] // Timing
> > t5[12] // Timing
> >
> > {27., 68302634401}
> >
> > {17.187, 68302634401}
> >
> > T2c[13] // Timing
> > t5[13] // Timing
> >
> > {150.172, 924972048001}
> >
> > {64.032, 924972048001}
> >
> > t5[14] // Timing
> >
> > {516.546, 48318396825601}
> >
> > The trick (already used by someone else, I think)
> >
> > lldaux5[x_List] /; MemberQ[x, 1] :=
> > Count[x, 1] + lldaux5@DeleteCases[x, 1]
> >
> > means that if the factor counts includes n ones, the LP problem used by
> > T2c has 2^n times as many variables as the one used by t5. Yet this
> > yielded only a 10-15% improvement, by itself.
> >
> > A bigger gain was achieved in factorCounts, based on one of Carl Woll's
> > ideas in
> >
> > http://forums.wolfram.com/mathgroup/archive/2007/Jul/msg00411.html
> >
> > factorCounts[k_Integer] :=
> > If[EvenQ@k, factorCounts[(k + 2)/2, k - 1],
> > factorCounts[(k - 1)/2, k + 2]]
> >
> > Several fine adjustments were required to get any improvement at all.
> > Defining factorCounts for 1,2,3,4, and 7 removed steps from the code for
> > other cases, and it was crucial to take full advantage of the fact that
> > the GCD of the two factors could only be 1 or 3.
> >
> > Bobby
> >
> > On Tue, 07 Aug 2007 00:31:22 -0500, sashap <pavlyk at gmail.com> wrote:
> >
> > > On Aug 5, 3:58 am, Carl Woll <ca... at wolfram.com> wrote:
> > >> sashap wrote:
> > >> >Carl's code is very good on numbers not involving
> > >> >products of large powers of primes. Otherwise it
> > >> >it is memory intensive and not as efficient:
> > >>
> > >> >In[109]:= Table[Timing[LargestPartition[{k, k}]], {k, 7, 15}]
> > >>
> > >> >Out[109]=
> > >> >{{0.031,7},{0.141,7},{0.172,8},{0.187,9},{1.531,9},{1.875,10},
> > >> >{12.016,10},{16.234,11},{141.391,11}}
> > >>
> > >> >One can use LinearProgramming to improve on the
> > >> >situation.
> > >>
> > >> >Additional improvement comes from the following
> > >> >observation. The longest factorization can be built
> > >> >by taking the longest sequence of divisors of degree 1,
> > >> >then of degree 2 and so on. The degree of the divisor
> > >> >is defined as
> > >>
> > >> > deg[n_] := Total[ FactorInteger[n][[All,2]] ]
> > >>
> > >> >In other words, let n == p1^e1 *...* pn^en.
> > >> >First take {p1, p2,.., pn}, then the longest
> > >> >sequence of pairs with repetitions and so on.
> > >>
> > >> Nice work! However, I think there is a problem with your algorithm.
> > >> There are many possible ways to a longest sequence of (distinct) pairs
> > >> with repetitions. It is possible that some of these sequences will not
> > >> produce the longest factorization. For instance, consider 2^10 * 3^3 *
> > >> 5^2. After removing the degree 1 factors we are left with 2^9 * 3^2 * 5.
> > >> Two possible length 3 sequences of pairs are:
> > >>
> > >> a) 2^2, 2*3, 2*5 leaving 2^5*3
> > >>
> > >> and
> > >>
> > >> b) 2^2, 2*3, 3*5 leaving 2^6
> > >>
> > >> Now, for case a) we can create two more unique factors, 2^3 and 2^2*3,
> > >> while for case b) we can't create two more unique factors.
> > >>
> > >> So, I think one more thing is needed in your algorithm, a way of knowing
> > >> which longest sequence of pairs to pick.
> > >>
> > >> Now, it turns out that the above example is handled correctly by your
> > >> algorithm, that is, your algorithm by design or chance picks a longest
> > >> sequence that leads to a longest factorization. I played around with a
> > >> bunch of different examples, and came up with one example where your
> > >> algorithm picks a longest sequence that doesn't lead to a longest
> > >> factorization. This wasn't easy because my algorithm is soo slow (but I
> > >> think, correct).
> > >>
> > >> In[594]:= counterexample = {2,3,4,5,6,7,8,10,12,14,15,35};
> > >>
> > >> In[595]:= Times @@ counterexample
> > >>
> > >> Out[595]= 35562240000
> > >>
> > >> In[596]:= Length[counterexample]
> > >>
> > >> Out[596]= 12
> > >>
> > >> In[597]:= LengthOfLongestDecomposition[Times @@ counterexample]
> > >>
> > >> Out[597]= 11
> > >>
> > >> My guess is that a simple heuristic probably exists which will allow you
> > >> to pick a good longest sequence instead of a bad longest sequence.
> > >> Perhaps something like choosing the longest sequence that keeps as many
> > >> different types of prime factors available for the next stage? If so,
> > >> then the biggest bottleneck will be just factoring the triangular
> > >> numbers.
> > >>
> > >> Carl
> > >>
> > >
> > > Carl,
> > >
> > > good points ! Indeed:
> > >
> > > In[98]:= ffaux /@ Permutations[FactorInteger[chi][[All, 2]] ]
> > >
> > > Out[98]= {12,12,11,12,12,12,12,12,12,11,11,11}
> > >
> > > The heuristics you mention might be different sorting of exponents,
> > > even though we can not guarantee it, that is
> > > changing LengthOfLongestDecomposition to the following:
> > >
> > > LengthOfLongestDecomposition[nn_Integer] :=
> > > ffaux[Reverse@Sort@FactorInteger[nn][[All, 2]]]
> > >
> > > Another approach is to write n == d1^s1 * ... * dk^sk and just use
> > > LinearProgramming once. Here
> > > s1, ..., sk are either 0 or 1, and d1, ..., dk are all distinct
> > > divisors of the given integer n.
> > >
> > > Clear[lldaux, lld];
> > > lldaux[Lpow_] := (lldaux[Lpow] =
> > > With[{Mtup = Tuples[ Range[0, #] & /@ Lpow]},
> > > Total[
> > > Quiet@LinearProgramming[ConstantArray[-1, Length[Mtup]],
> > > Transpose@Mtup, Thread@{Lpow, 0},
> > > Array[{0, 1} &, Length[Mtup]], Integers]] - 1])
> > >
> > > lld[n_Integer] := lldaux[FactorInteger[n][[All, 2]] // Sort]
> > >
> > > T2c[n_] := # (# + 1)/2 &@
> > > NestWhile[# + 1 &, 2, lld[# (# + 1)/2 - 1] < n &]
> > >
> > > The performance of T2c is competitive to Carl's code for small values
> > > of the argument
> > > and has an edge over it for large ones:
> > >
> > > In[168]:= {Timing@T2[11], Timing@T2c[11]}
> > >
> > > Out[168]= {{3.829,2080995841},{3.921,2080995841}}
> > >
> > > In[169]:= {Timing@T2[12], Timing@T2c[12]}
> > >
> > > Out[169]= {{26.532,68302634401},{22.797,68302634401}}
> > >
> > > In[170]:= {Timing@T2[13], Timing@T2c[13]}
> > >
> > > Out[170]= {{144.406,924972048001},{123.875,924972048001}}
> > >
> > > Oleksandr Pavlyk and Maxim Rytin
> > >
> > >> >In order to choose the longest sequence of factors of a given degree,
> > >> >we set this up as an integer linear programming problem. We illustrate
> > >> >this for degree 2. Let p1*p1, p1*p2, and so on be candidate factors of
> > >> >degree 2. Then
> > >>
> > >> > (p1*p1)^s1 (p1*p2)^s2 ...
> > >>
> > >> >must divide the original number. Additionally, each sk is either 0 or
> > >> >1.
> > >> >This gives us the constraints for variables s1, s2, ... and
> > >> >the objective function being maximized is s1+s2+....
> > >>
> > >> >The code implementing those ideas:
> > >>
> > >> >CombinationsWithRepetitions =
> > >> > Compile[{{n, _Integer}, {k, _Integer}},
> > >> > Module[{ans, i = 1, j = 1, l = 1},
> > >> > ans = Array[0 &, {Binomial[n + k - 1, k], k}];
> > >> > While[True, While[j <= k, ans[[i, j++]] = l];
> > >> > If[i == Length@ans, Break[]];
> > >> > While[ans[[i, --j]] == n,];
> > >> > l = ans[[i++, j]] + 1;
> > >> > ans[[i]] = ans[[i - 1]];];
> > >> > ans]];
> > >>
> > >> >Clear[LengthOfLongestDecomposition, ffaux]
> > >> >LengthOfLongestDecomposition[nn_] :=
> > >> > ffaux[Sort@FactorInteger[nn][[All, 2]]]
> > >> >ffaux[$Lpow_] :=
> > >> > ffaux[$Lpow] =
> > >> > Module[{Lpow = $Lpow, Ldiv, n, m, k = 1, Lind, ans = 0},
> > >> > n = Length@Lpow;
> > >> > While[Total@Lpow >= k,
> > >> > Ldiv = CombinationsWithRepetitions[n, k++];
> > >> > m = Length@Ldiv;
> > >> > Lind =
> > >> > Quiet@LinearProgramming[ConstantArray[-1, m],
> > >> > Total[1 - Unitize[Ldiv - #], {2}] & /@ Range@n,
> > >> > Thread@{Lpow, -1}, Array[{0, 1} &, m], Integers];
> > >> > ans += Total@Lind;
> > >> > Lpow -= BinCounts[Flatten@Pick[Ldiv, Lind, 1], {Range[n + 1]}]];
> > >> > ans]
> > >>
> > >> >Compare the timing given earlier with the following
> > >>
> > >> >In[132]:= Table[Timing[ffaux[{k, k}]], {k, 7, 15}]
> > >>
> > >> >Out[132]= \
> > >> >{{0.016,7},{1.78746*10^-14,7},{1.78746*10^-14,8},{0.015,9},{3.15303*\
> > >> >10^-14,9},{0.016,10},{0.,10},{0.015,11},{0.,11}}
> > >>
> > >> >Clearly, the use of heavy machinery of LinearProgramming comes at the
> > >> >cost of
> > >> >noticeable overhead which is why the performance of this algorithm is
> > >> >comparable
> > >> >to that of Carl's code when measured over a sequence of triangular
> > >> >numbers because
> > >> >problematic numbers are rare.
> > >>
> > >> >T2b[n_] := # (# + 1)/2 &@
> > >> > NestWhile[# + 1 &, 1,
> > >> > LengthOfLongestDecomposition[# (# + 1)/2 - 1] < n &]
> > >>
> > >> >In[66]:= {Timing[T2[11]], Timing[T2b[11]]}
> > >>
> > >> >Out[66]= {{3.172,2080995841},{2.328,2080995841}}
> > >>
> > >> >In[67]:= {Timing[T2[12]], Timing[T2b[12]]}
> > >>
> > >> >Out[67]= {{26.312,68302634401},{18.235,68302634401}}
> > >>
> > >> >In[68]:= {Timing[T2[13]], Timing[T2b[13]]}
> > >>
> > >> >Out[68]= {{142.765,924972048001},{111.422,924972048001}}
> > >>
> > >> >Oleksandr Pavlyk and Maxim Rytin
> > >>
> > >> >On Jul 10, 5:37 am, Carl Woll <ca... at wolfram.com> wrote:
> > >>
> > >> >>Andrzej and Diana,
> > >>
> > >> >>Here is a faster algorithm. First, an outline:
> > >>
> > >> >>For a given number, the easy first step in writing it as a
> > >> >>factorization of the most distinct factors is to use each prime as a
> > >> >>factor. Then, the hard part is to take the remaining factors and
> > >> >>partition them in a way to get the most distinct factors. However,
> > >> note
> > >> >>that this step only depends on the counts of each prime factor, and
> > >> not
> > >> >>on the values of the primes themselves. Hence, we can memoize the
> > >> number
> > >> >>of distinct factors possible for a set of counts. I do this as
> > >> follows:
> > >>
> > >> >>Let f be the list of remaining factors, of length l.
> > >>
> > >> >>1. Determine possible integer partitions of l, with the smallest
> > >> >>partition allowed being 2. We also need to order these partitions by
> > >> >>length, from largest to smallest:
> > >>
> > >> >>partitions = IntegerPartitions[l, All, Range[2, l]];
> > >> >>partitions = Reverse@partitions[[ Ordering[Length/@partitions] ]];
> > >>
> > >> >>2. Now, we need to test each partition to see if it's possible to fill
> > >> >>in the partitions with the factors such that each partition is unique.
> > >> >>Once we find such a partition, we are done, and we know how many
> > >> >>distinct factors that number can be written as. This step I do by
> > >> >>recursion, i.e., take the first member of a partition and fill it in
> > >> >>with one of the possible subsets of factors, and then repeat with the
> > >> >>remaining members of a partition. I do this with the function
> > >> Partition=
> > > able.
> > >>
> > >> >>Here is the code:
> > >>
> > >> >>LargestPartition[list : {1 ..}] := Length[list]
> > >> >>LargestPartition[list_List] := Length[list] +
> > >> >>LargestTwoPartition[Reverse@Sort@list - 1 /. {a__, 0 ..} -> {a}]
> > >>
> > >> >>Clear[LargestTwoPartition]
> > >> >>LargestTwoPartition[list_] := LargestTwoPartition[list] = Module[{=
> > > set,
> > >> >>partitions, res},
> > >> >> set = CountToSet[list];
> > >> >> partitions = IntegerPartitions[Total[list], All, Range[2, Total[li=
> > > st]]];
> > >> >> partitions = Reverse@partitions[[Ordering[Length /@ partitions]]];
> > >> >> res = Cases[partitions, x_ /; Partitionable[{}, set, x], 1, 1];
> > >> >> If[res === {},
> > >> >> 0,
> > >> >> Length@First@res
> > >> >> ]
> > >> >>]
> > >>
> > >> >>Partitionable[used_, unused_, part_] := Module[{first, rest},
> > >> >> first = Complement[DistinctSubsets[unused, First@part], used];
> > >> >> If[first === {}, Return[False]];
> > >> >> rest = CountToSet /@ Transpose[
> > >> >> SetToCount[unused, Max[unused]] -
> > >> >> Transpose[SetToCount[#, Max[unused]] & /@ first]
> > >> >> ];
> > >> >> Block[{Partitionable},
> > >> >> Or @@ MapThread[
> > >> >> Partitionable[Join[used, {#1}], #2, Rest[part]] &,
> > >> >> {first, rest}
> > >> >> ]
> > >> >> ]
> > >> >>]
> > >>
> > >> >>Partitionable[used_, rest_, {last_}] := ! MemberQ[used, rest]
> > >>
> > >> >>CountToSet[list_] := Flatten@MapIndexed[Table[#2, {#1}] &, list]
> > >> >>SetToCount[list_, max_] := BinCounts[list, {1, max + 1}]
> > >>
> > >> >>DistinctSubsets[list_, len_] := Union[Subsets[list, {len}]]
> > >>
> > >> >>T2[n_] := Module[{k=1},
> > >> >> While[k++; LargestPartition[FactorInteger[k*((k + 1)/2) - 1][[All,
> > >> >>2]]] < n];
> > >> >> k*((k + 1)/2)
> > >> >>]
> > >>
> > >> >>Then:
> > >>
> > >> >>In[11]:= T2[6] // Timing
> > >> >>Out[11]= {0.047,13861}
> > >>
> > >> >>In[12]:= T2[7] // Timing
> > >> >>Out[12]= {0.031,115921}
> > >>
> > >> >>In[13]:= T2[8] // Timing
> > >> >>Out[13]= {0.11,665281}
> > >>
> > >> >>In[14]:= T2[9] // Timing
> > >> >>Out[14]= {0.625,18280081}
> > >>
> > >> >>In[15]:= T2[10] // Timing
> > >> >>Out[15]= {1.157,75479041}
> > >>
> > >> >>In[16]:= T2[11] // Timing
> > >> >>Out[16]= {5.875,2080995841}
> > >>
> > >> >>In[17]:= T2[12] // Timing
> > >> >>Out[17]= {48.703,68302634401}
> > >>
> > >> >>For Diana, note that
> > >>
> > >> >>In[20]:= 481 482/2 - 1 == 115921 - 1 == 2 3 4 5 6 7 23
> > >> >>Out[20]= True
> > >>
> > >> >>so 115921-1 can indeed be written as a product of 7 distinct factors.
> > >>
> > >> >>Partitionable can be improved, but another bottleneck for larger cases
> > >> >>is evaluating FactorInteger. One possibility for improving
> > >> FactorInteger
> > >> >>speed is to note that
> > >>
> > >> >>In[21]:= n (n + 1)/2 - 1 == (n + 2) (n - 1)/2 // Expand
> > >>
> > >> >>Out[21]= True
> > >>
> > >> >>So, rather than applying FactorInteger to n(n+1)/2-1 you can instead
> > >> >>somehow combine FactorInteger[n+2] and FactorInteger[n-1].
> > >>
> > >> >>At any rate, it takes a bit longer, but with enough patience one can
> > >> >>also find:
> > >>
> > >> >>In[53]:= 1360126 1360127/2 - 1 == 924972048001 - 1 == 2 3 4 =
> > > 5 6 7 10 11
> > >> >>12 13 15 23 31
> > >> >>Out[53]= True
> > >>
> > >> >>and
> > >>
> > >> >>In[56]:= 9830401 9830402/2 - 1 == 48318396825601 - 1 == 2 3 =
> > > 4 5 6 8 9
> > >> >>10 11 12 17 22 32 59
> > >> >>Out[56]= True
> > >>
> > >> >>Carl Woll
> > >> >>Wolfram Research
> > >>
> > >> >>Andrzej Kozlowski wrote:
> > >>
> > >> >>>Well, I stayed up longer than I wanted and I think I have now fixed
> > >> >>>it, I hope for the final time. Here is the new FF:
> > >>
> > >> >>>FFF[n_] := Module[{u = FactorInteger[n], s, k, partialQ, finalQ, s=
> > > pace},
> > >> >>> s = u[[All,2]]; k = Length[u]; partialQ[l_List] :=
> > >> >>> And @@ Flatten[{Last[l] == Array[0 & , k] ||
> > >> >>> !MemberQ[Most[l], Last[l]], Thread[Total[l] <= s - 1]}];
> > >> >>> finalQ[l_List] := And @@ Flatten[{Last[l] == Array[0 & , k] =
> > > ||
> > >> >>> !MemberQ[Most[l], Last[l]], Total[l] == s - 1}];
> > >> >>> space =
> > >>
> > >> ...
> > >>
> > >> read more =BB
> > >
> > >
> > >
> > >
> >
> >
> >
> > --
> > DrMajorBob at bigfoot.com
> >
>
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