Shanks’ SQUFOF according to McMath

Posted on 2021/07/30 by wdjoyner

In 2003, a math major named Steven McMath approached Fred Crabbe and I about directing his Trident thesis. (A Trident is like an honors thesis, but the student gets essentially the whole year to focus on writing the project.) After he graduated, I put a lot of his work online at the USNA website. Of course, I’ve retired since then and the materials were removed. This blog post is simply to try to repost a lot of the materials which arose as part of his thesis (which are, as official works of the US government, all in the public domain; of course, Dan Shanks notes are copyright of his estate and are posted for scholarly use only).

McMath planned to study Dan Shanks‘ SQUFOF method, which he felt could be exploited more than had been so far up to that time (in 2004). This idea had a little bit of a personal connection for me. Dan Shanks was at the University of Maryland during the time (1981-1983) I was a grad student there. While he and I didn’t talk as much as I wish we had, I remember him as friendly guy, looking a lot like the picture below, with his door always open, happy to discuss mathematics.

One of the first things McMath did was to type up the handwritten notes we got (I think) from Hugh Williams and Duncan Buell. A quote from a letter from Buell:

“Dan probably invented SQUFOF in 1975. He had just bought an HP 67 calculator, and the algorithm he invented had the advantages of being very simple to program, so it would fit on the calculator, very powerful as an algorithm when measured against the size of the program, and it factors double precision numbers using single precision arithmetic.“

Here are some papers on this topic (with thanks to Michael Hortmann for the references to Gower’s papers):

David Joyner, Notes on indefinite integral binary quadratic forms with SageMath, preprint 2004. pdf

F. Crabbe, D. Joyner, S. McMath, Continued fractions and Parallel SQUFOF, 2004. arxiv

D. Shanks, Analysis and Improvement of the Continued Fraction Method of Factorization, handwritten notes 1975, typed into latex by McMath 2004. pdf.

D. Shanks, An Attempt to Factor N = 1002742628021, handwritten notes 1975, typed into latex by McMath 2004. pdf.

D. Shanks, SQUFOF notes, handwritten notes 1975, typed into latex by McMath 2004. pdf.

S. McMath, Daniel Shanks’ Square Forms Factorization, 2004 preprint. pdf1, pdf2.

S. McMath, Parallel Integer Factorization Using Quadratic Forms, Trident project, 2004. pdf.

Since that time, many excellent treatments of SQUFOF have been presented. For example, see

J. Gower, S. Wagstaff, Square Form Factorization, Math Comp, 2008. pdf.

J. Gower, Square Form Factorization, PhD Thesis, December 2004. pdf.

How do I construct … in GAP?

Posted on 2017/08/09 by wdjoyner

This page is devoted to answering some basic questions along the line “How do I construct … in GAP?” You may view the html source code for the GAP commands without the output or GAP prompt.

Please send suggestions, additions, corrections to David Joyner.

This page itself is under construction…

Questions

How do I construct a … group? permutation dihedral cyclic conjugacy classes of a finitely presented	How do I … a polynomial? factor find roots of evaluate Groebner basis of ideal of
Brauer characters	How do I find the … of a group representation? character table
How do I compute an mod m, where A is …? an integer a square matrix a polynomial	Given a group G, how do I compute … ? all normal subgroups of G all abelian subgroups of G a homomorphism from G to another group H the semi-direct product of G with another group H all groups of order 12 written as semi-direct products the cohomology of G with coefficients in A

Answers

- permutation:
  To construct a permutation group, write down generators in disjoint cycle notation, put them in a list (i.e., surround them by square brackets), and the permutation group G generated by the cycles (1,2)(3,4) and (1,2,3):

gap> G:=Group((1,2)(3,4),(1,2,3));

Group([ (1,2)(3,4), (1,2,3) ])

This is of course a subgroup of the symmetric group S₄ on 4 letters. Indeed, this G is in fact the alternating group on four letters, A₄.

By virtue of the fact that the permutations generating G employ integers less than or equal to 4, this group G is a subgroup of the symmetric group S₄ on 4 letters. Some permutation groups have special constructions:

gap> S4:=SymmetricGroup(4);
Sym( [ 1 .. 4 ] )
gap> A4:=AlternatingGroup(4);
Alt( [ 1 .. 4 ] )
gap> IsSubgroup(S4,G);
true
gap> IsSubgroup(A4,G);
true
gap> S3:=SymmetricGroup(3);
Sym( [ 1 .. 3 ] )
gap> IsSubgroup(S3,G);
false

- dihedral
  To construct a dihedral group, use the special “DihedralGroup” command:

gap> G:=DihedralGroup(6);
gap> Size(G);
6
gap> f:=GeneratorsOfGroup( G );
[ f1, f2 ]
gap> f[1]^2; f[2]^3;
identity of ...
identity of ...
gap> f[1]^2= f[2]^3;
true

cyclic group
To construct a cyclic group, you may construct integers mod n:

gap> R:=ZmodnZ( 12);
(Integers mod 12)
gap> a:=Random(R);
ZmodnZObj( 11, 12 )
gap> 4*a;
ZmodnZObj( 8, 12 )
gap> b:=Random(R);
ZmodnZObj( 9, 12 )
gap> a+b;
ZmodnZObj( 8, 12 )

or use the special “CyclicGroup” command

gap> G:=CyclicGroup(12);
pc group of size 12 with 3 generators
gap> a:=Random(G);
f3^2
gap> f:=GeneratorsOfGroup( G );
[ f1, f2, f3 ]
gap> f[1]^4;
f3
gap> f[1]^12;
identity of ...

conjugacy:
The conjugacy classes of a group G are computed using the “ConjugacyClasses” command. This is a list of classes {x^-1*g*x | x in G}.

gap> G:=SL(2,7);
SL(2,7)
gap> CG:=ConjugacyClasses(G);
[ [ [ Z(7)^0, 0*Z(7) ], [ 0*Z(7), Z(7)^0 ] ]^G,
  [ [ 0*Z(7), Z(7)^3 ], [ Z(7)^0, Z(7)^5 ] ]^G,
  [ [ 0*Z(7), Z(7)^4 ], [ Z(7)^5, Z(7)^5 ] ]^G,
  [ [ Z(7)^3, 0*Z(7) ], [ 0*Z(7), Z(7)^3 ] ]^G,
  [ [ 0*Z(7), Z(7)^3 ], [ Z(7)^0, Z(7)^2 ] ]^G,
  [ [ 0*Z(7), Z(7)^4 ], [ Z(7)^5, Z(7)^2 ] ]^G,
  [ [ 0*Z(7), Z(7)^3 ], [ Z(7)^0, 0*Z(7) ] ]^G,
  [ [ 0*Z(7), Z(7)^3 ], [ Z(7)^0, Z(7)^4 ] ]^G,
  [ [ 0*Z(7), Z(7)^3 ], [ Z(7)^0, Z(7) ] ]^G,
  [ [ Z(7)^4, 0*Z(7) ], [ 0*Z(7), Z(7)^2 ] ]^G,
  [ [ Z(7)^5, 0*Z(7) ], [ 0*Z(7), Z(7) ] ]^G ]
gap> g:=Representative(CG[3]); Order(g);
[ [ 0*Z(7), Z(7)^4 ], [ Z(7)^5, Z(7)^5 ] ]
14
gap> g:=Representative(CG[4]); Order(g);
[ [ Z(7)^3, 0*Z(7) ], [ 0*Z(7), Z(7)^3 ] ]
2
gap> g:=Representative(CG[5]); Order(g);
[ [ 0*Z(7), Z(7)^3 ], [ Z(7)^0, Z(7)^2 ] ]
7
gap> g:=Representative(CG[6]); Order(g);
[ [ 0*Z(7), Z(7)^4 ], [ Z(7)^5, Z(7)^2 ] ]
7
gap>

presented
To construct a finitely presented group in GAP, use the “FreeGroup” and “FpGroupPresentation” commands. Here is one example.

gap> M12 := MathieuGroup( 12 );
Group([ (1,2,3,4,5,6,7,8,9,10,11), (3,7,11,8)(4,10,5,6), (1,12)(2,11)(3,6)(4,8)(5,9)(7,10) ])
gap> F := FreeGroup( "a", "b", "c" );
free group on the generators [ a, b, c ]
gap> words := [ F.1, F.2 ];
[ a, b ]
gap> P := PresentationViaCosetTable( M12, F, words );
presentation with 3 gens and 10 rels of total length 97
gap> TzPrintRelators( P );
#I  1. c^2
#I  2. b^4
#I  3. a*c*a*c*a*c
#I  4. a*b^2*a*b^-2*a*b^-2
#I  5. a^11
#I  6. a^2*b*a^-2*b^2*a*b^-1*a^2*b^-1
#I  7. a*b*a^-1*b*a^-1*b^-1*a*b*a^-1*b*a^-1*b^-1
#I  8. a^2*b*a^2*b^2*a^-1*b*a^-1*b^-1*a^-1*b^-1
#I  9. a*b*a*b*a^2*b^-1*a^-1*b^-1*a*c*b*c
#I  10. a^4*b*a^2*b*a^-2*c*a*b*a^-1*c
gap> G := FpGroupPresentation( P );
fp group on the generators [ a, b, c ]
gap> RelatorsOfFpGroup( G );  
[ c^2, b^4, a*c*a*c*a*c, a*b^-2*a*b^-2*a*b^-2, a^11, a^2*b*a^-2*b^-2*a*b^-1*a^2*b^-1, a*b*a^-1*b*a^-1*b^-1*a*b*a^-1*b*a^-1*b^-1,
  a^2*b*a^2*b^-2*a^-1*b*a^-1*b^-1*a^-1*b^-1, a*b*a*b*a^2*b^-1*a^-1*b^-1*a*c*b*c, a^4*b*a^2*b*a^-2*c*a*b*a^-1*c ]
gap> Size(M12);
95040
gap> Size(G);
95040
gap> IsomorphismGroups(G,M12); 
????????

The last command is computationally intensive and requires more than the default memory allocation of 256M of RAM.

Here is another example.

gap> F := FreeGroup( "a", "b");
free group on the generators [ a, b ]
gap> G:=F/[F.1^2,F.2^3,F.1*F.2*F.1^(-1)*F.2^(-1)];
fp group on the generators [ a, b ]
gap> Size(G);
6

rref
The key command for row reduction is “TriangulizeMat”. The following example illustrates the syntax.

gap> M:=[[1,2,3,4,5],[1,2,1,2,1],[1,1,0,0,0]];
[ [ 1, 2, 3, 4, 5 ], [ 1, 2, 1, 2, 1 ], [ 1, 1, 0, 0, 0 ] ]
gap> TriangulizeMat(M);
gap> M;
[ [ 1, 0, 0, -1, 1 ], [ 0, 1, 0, 1, -1 ], [ 0, 0, 1, 1, 2 ] ]
gap> Display(M);
[ [   1,   0,   0,  -1,   1 ],
  [   0,   1,   0,   1,  -1 ],
  [   0,   0,   1,   1,   2 ] ]
gap> M:=Z(3)^0*[[1,2,3,4,5],[1,2,1,2,1],[1,1,0,0,0]];
[ [ Z(3)^0, Z(3), 0*Z(3), Z(3)^0, Z(3) ],
  [ Z(3)^0, Z(3), Z(3)^0, Z(3), Z(3)^0 ],
  [ Z(3)^0, Z(3)^0, 0*Z(3), 0*Z(3), 0*Z(3) ] ]
gap> TriangulizeMat(M);
gap> Display(M);
 1 . . 2 1
 . 1 . 1 2
 . . 1 1 2
gap>

kernel:
There are different methods for matrices over the integers and matrices over a field. For integer entries, related commands include “NullspaceIntMat” and “SolutionNullspaceIntMat” in section 25.1 “Linear equations over the integers and Integral Matrices” of the reference manual.

gap> M:=[[1,2,3],[4,5,6],[7,8,9]];
[ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 7, 8, 9 ] ]
gap> NullspaceIntMat(M);
[ [ 1, -2, 1 ] ]
gap> SolutionNullspaceIntMat(M,[0,0,1]);
[ fail, [ [ 1, -2, 1 ] ] ]
gap> SolutionNullspaceIntMat(M,[0,0,0]);
[ [ 0, 0, 0 ], [ [ 1, -2, 1 ] ] ]
gap> SolutionNullspaceIntMat(M,[1,2,3]);
[ [ 1, 0, 0 ], [ [ 1, -2, 1 ] ] ]

Here (0,0,1) is not in the image of M
(under v-> v*M) but (0,0,0) and (1,2,3) are.

For field entries, related commands include “NullspaceMat” and “TriangulizedNullspaceMat” in section 24.6 “Matrices Representing Linear Equations and the Gaussian Algorithm”
of the reference manual.

gap> M:=[[1,2,3],[4,5,6],[7,8,9]];
[ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 7, 8, 9 ] ]
gap> NullspaceMat(M);
[ [ 1, -2, 1 ] ]
gap> TriangulizedNullspaceMat(M);
[ [ 1, -2, 1 ] ]
gap> M:=[[1,2,3,1,1],[4,5,6,1,1],[7,8,9,1,1],[1,2,3,1,1]];
[ [ 1, 2, 3, 1, 1 ], [ 4, 5, 6, 1, 1 ], [ 7, 8, 9, 1, 1 ], 
  [ 1, 2, 3, 1, 1 ] ]
gap> NullspaceMat(M);
[ [ 1, -2, 1, 0 ], [ -1, 0, 0, 1 ] ]
gap> TriangulizedNullspaceMat(M);
[ [ 1, 0, 0, -1 ], [ 0, 1, -1/2, -1/2 ] ]

characteristic polynomial:
Please see section 24.12.1 of the GAP reference manual for examples of characteristic polynomial of a square matrix (“CharacteristicPolynomial”) and section 56.3 for examples of the “characteristic polynomial” (called a “TracePolynomial”) of an element of a field extension.

character:
GAP contains very extensive character theoretic functions and data libraries (including an interface the character table in the Atlas). Here is just one simple example.

gap> G:=Group((1,2)(3,4),(1,2,3));
Group([ (1,2)(3,4), (1,2,3) ])
gap> T:=CharacterTable(G);
CharacterTable( Alt( [ 1 .. 4 ] ) )
gap> Display(T);
CT1

     2  2  2  .  .
     3  1  .  1  1

       1a 2a 3a 3b
    2P 1a 1a 3b 3a
    3P 1a 2a 1a 1a

X.1     1  1  1  1
X.2     1  1  A /A
X.3     1  1 /A  A
X.4     3 -1  .  .

A = E(3)^2
  = (-1-ER(-3))/2 = -1-b3
gap> irr:=Irr(G);
[ Character( CharacterTable( Alt( [ 1 .. 4 ] ) ), [ 1, 1, 1, 1 ] ),
  Character( CharacterTable( Alt( [ 1 .. 4 ] ) ), [ 1, 1, E(3)^2, E(3) ] ),
  Character( CharacterTable( Alt( [ 1 .. 4 ] ) ), [ 1, 1, E(3), E(3)^2 ] ),
  Character( CharacterTable( Alt( [ 1 .. 4 ] ) ), [ 3, -1, 0, 0 ] ) ]
gap> Display(irr);
[ [       1,       1,       1,       1 ],
  [       1,       1,  E(3)^2,    E(3) ],
  [       1,       1,    E(3),  E(3)^2 ],
  [       3,      -1,       0,       0 ] ]
gap> chi:=irr[2]; gamma:=CG[3]; g:=Representative(gamma); g^chi;
Character( CharacterTable( Alt( [ 1 .. 4 ] ) ), [ 1, 1, E(3)^2, E(3) ] )
(1,2,3)^G
(1,2,3)
E(3)^2

For further details and examples, see chapters 69– 72 of the GAP reference manual.

brauer:
Just a simple example of what GAP can do here. To construct a Brauer character table:

gap> G:=Group((1,2)(3,4),(1,2,3));
Group([ (1,2)(3,4), (1,2,3) ])
gap> irr:=IrreducibleRepresentations(G,GF(7));
[ [ (1,2)(3,4), (1,2,3) ] -> [ [ [ Z(7)^0 ] ], [ [ Z(7)^0 ] ] ],
  
[ (1,2)(3,4), (1,2,3) ] -> [ [ [ Z(7)^0 ] ], [ [ Z(7)^4 ] ] ],
  
[ (1,2)(3,4), (1,2,3) ] -> [ [ [ Z(7)^0 ] ], [ [ Z(7)^2 ] ] ],
  
[ (1,2)(3,4), (1,2,3) ] -> [
      
[ [ 0*Z(7), Z(7)^3, Z(7)^0 ], [ 0*Z(7), Z(7)^3, 0*Z(7) ], 
[ Z(7)^0, Z(7)^3, 0*Z(7) ] ],
      [ [ 0*Z(7), Z(7)^0, 0*Z(7) ], 
[ 0*Z(7), 0*Z(7), Z(7)^0 ], [ Z(7)^0, 0*Z(7), 0*Z(7) ] ]
     
] ]
gap> brvals := List(irr,chi-> List(ConjugacyClasses(G),c->
BrauerCharacterValue(Image(chi, Representative(c)))));
[ [ 1, 1, 1, 1 ], [ 1, 1, E(3)^2, E(3) ], [ 1, 1, E(3), E(3)^2 ], 
[ 3, -1, 0, 0 ] ]
gap> Display(brvals);
[ [       1,       1,       1,       1 ],
  
[       1,       1,  E(3)^2,    E(3) ],
  
[       1,       1,    E(3),  E(3)^2 ],
  
[       3,      -1,       0,       0 ] ]
gap>

List(ConjugacyClasses(G),c->BrauerCharacterValue(Image(chi, Representative(c)))));
#Display(brvals);
T:=CharacterTable(G);
Display(T);
–>

polynomial
There are various ways to construct a polynomial in GAP.

gap> Pts:=Z(7)^0*[1,2,3];
[ Z(7)^0, Z(7)^2, Z(7) ]
gap> Vals:=Z(7)^0*[1,2,6];
[ Z(7)^0, Z(7)^2, Z(7)^3 ]
gap> g:=InterpolatedPolynomial(GF(7),Pts,Vals);
Z(7)^5*x_1^2+Z(7)

Or:

gap> p:=3;; F:=GF(p);;
gap> R:=PolynomialRing(F,["x1","x2"]);
PolynomialRing(..., [ x1, x2 ])
gap> vars:=IndeterminatesOfPolynomialRing(R);;
gap> x1:=vars[1]; x2:=vars[2];
x1
x2
gap> p:=x1^5-x2^5;
x1^5-x2^5
gap> DivisorsMultivariatePolynomial(p,R);
[ x1^4+x1^3*x2+x1^2*x2^2+x1*x2^3+x2^4, x1-x2 ]

Or:

gap> x:=X(Rationals);
x_1
gap> f:=x+x^2+1;
x_1^2+x_1+1
gap> Value(f,[x],[1]);
3

factor
To factor a polynomial in GAP, there is one command for univariate polynomials (“Factors”) and another command for multivariate polynomials (“DivisorsMultivariatePolynomial”). For a factoring a univariate polynomial, GAP provides only methods over finite fields and over subfields of cyclotomic fields. Please see the examples given in section 64.10 “Polynomial Factorization” for more details. For multivariate polynomials, a very slow algorithm has been implemented in GAP and an interface to a very fast algorithm in Singular has been implemented for those who have both Singular and the GAP Singular package installed. The former of these was illustrated above in “polynomial” above. (Again, the ground field must be a finite field or a subfields of cyclotomic fields.) For the latter, please see the example in the (GAP-)Singular manual FactorsUsingSingularNC.
roots
There are some situations where GAP can find the roots of a polynomial but GAP does not do this generally. (The roots must generate either a finite field or a subfield of a cyclotomic field.) However, there is a package called RadiRoot which must be installed which does help to do this for polynomials with rational coefficients (radiroot itself requires other packages to be installed; please see the webpage for more details). The “Factors” command actually has an option which allows you to increase the groundfield so that a factorization actually returns the roots. Please see the examples given in section 64.10 “Polynomial Factorization” for more details. Here is a second approach.
```
gap> p:=3; n:=4; F:=GF(p^n); c:=Random(F); r:=2;
3
4
GF(3^4)
Z(3^4)^79
2
gap>  x:=X(F,1); f:=x^r-c*x+c-1;
x_1
x_1^2+Z(3^4)^39*x_1+Z(3^4)^36
gap>  F_f:=FieldExtension( F, f );
AsField( GF(3^4), GF(3^8) )
gap>  alpha:=RootOfDefiningPolynomial(F_f);
Z(3^4)^36
gap> Value(f,[x],[alpha]);
0*Z(3)
```
Here is a third. First, enter the following program
```
RootOfPolynomial:=function(f,R)
 local F0,Ff,a;
 F0:=CoefficientsRing(R);
 Ff:=FieldExtension(F0,f);
 a:=RootOfDefiningPolynomial(Ff);
 return a;
end;
```
Here’s how this can be used to find a root:
```
gap> F:=Rationals;
Rationals
gap> x:=X(F,1); f:=x^2+x+1;
x_1
x_1^2+x_1+1
gap> R:=PolynomialRing( F, [ x ]);
PolynomialRing(..., [ x_1 ])
gap> a:=RootOfPolynomial(f,R);
E(3)
gap> # check:
gap> Value(f,[x],[a]);
0
```
Related links:
1. In the GAP Forum: Hensel lifting discussion.
2. In the manual, Galois groups.
evaluate:
The relevant command is “Value”. There are several examples already on this page. For others, please see the examples given in section 64.7 Multivariate polynomials of the manual. For sparse uivariate polynomials, there is also the command “ValuePol” in section 23.6 of the manual.

integer power
This is easy and intuitive:

gap> a:=1000; n:=100000; m:=123;
1000
100000
123
gap> a^n mod m;
1

matrix power:
This too is easy and intuitive:

gap> A:=[[1,2],[3,4]]; n:=100000; m:=123;
[ [ 1, 2 ], [ 3, 4 ] ]
100000
123
gap> A^n mod m;
[ [ 1, 41 ], [ 0, 1 ] ]

polynomial power
GAP allows you to do arithmetic over the polynomial ring R[x], where R = Z/nZ (where n is a positive integer). Here’s an example.

gap> Z4:=ZmodnZ(4);
(Integers mod 4)
gap> R:=UnivariatePolynomialRing(Z4,1);
PolynomialRing(..., [ x ])
gap> x:=IndeterminatesOfPolynomialRing(R)[1];
x
gap> I:=TwoSidedIdealByGenerators( R,[x^8-x^0]);
two-sided ideal in PolynomialRing(..., [ x ]), (1 generators)
gap> gen:=x^8-x^0;
x8-ZmodnZObj(1,4)
gap> QuotientRemainder(R,x^8,gen);
[ ZmodnZObj(1,4), ZmodnZObj(1,4) ]
gap> QuotientRemainder(R,x^15,gen);
[ x^7, x^7 ]
gap> QuotientRemainder(R,x^15+x^8,gen);
[ x^7+ZmodnZObj(1,4), x^7+ZmodnZObj(1,4) ]
gap> PowerMod( R, x+x^0, 15, gen );
ZmodnZObj(0,4)
gap> PowerMod( R, x, 15, gen );
x^7

Groebner basis
GAP’s Groebner bases algorithms are relatively slow and are included mostly for simple examples and for teaching purposes. However, a GAP interface to a very fast algorithm in Singular has been implemented for those who have both Singular and the GAP Singular package installed. The former of these is illustrated in section 64.17 Groebner bases of the GAP manual. For the latter, please see the example in the (GAP-)Singular manual GroebnerBasis.

normal subgroup:
Here is an example:

gap> G := AlternatingGroup( 5 );
Group( (1,2,5), (2,3,5), (3,4,5) )
gap> normal := NormalSubgroups( G );
[ Subgroup( Group( (1,2,5), (2,3,5), (3,4,5) ), [  ] ), 
  Subgroup( Group( (1,2,5), (2,3,5), (3,4,5) ), 
    [ (1,2)(3,4), (1,3)(4,5), (1,4)(2,3) ] ) ]

A water sharing problem and Sage

Posted on 2012/10/18 by wdjoyner

Here is the problem: There are n students, each with a cup that could contain water or could be empty. We assume that the cups have no limit and that there is at least one student with some water. Order the students from those who have the most water to those who have the least, with arbitrary choices in cases of ties. Following this order, each student takes turn sharing his water equally with the other students. In other words, a student with r ounces of water will give r/n ounces to each of the others (and hence have r/n ounces remaining for himself).

Q1: Is it possible that there is an initial configuration so that, at the end, all the students have exactly the same amount they started with?
This would mean that sharing was in fact not a loss.

Q2: Will this process “usually even out” the distribution of water?
(Part of the answer is to try to make this question more precise!)

Q3: Does the initial ordering of the students (ie, who pours when) matter?

This can be viewed as a matrix question. The action of the i-th student sharing water can be viewed as a linear transformation $A_i$ on the vector of water quantities, indexed by the students. I think that in general the answer to Q1 is “yes.” Here is the Sage code in the case of 3 students:

sage: A1 = matrix(QQ, [[1/3,0,0],[1/3,1,0],[1/3,0,1]])
sage: A2 = matrix(QQ, [[1,1/3,0],[0,1/3,0],[0,1/3,1]])
sage: A3 = matrix(QQ, [[1,0,1/3],[0,1,1/3],[0,0,1/3]])
sage: (A1*A2*A3).eigenvectors_right()                 
[(1, [(1, 2, 3)], 1), 
 (0.1851851851851852? - 0.05237828008789241?*I, 
 [(1, 0.?e-57 + 1.414213562373095?*I, -1.000000000000000? - 1.414213562373095?*I)], 1), 
 (0.1851851851851852? + 0.05237828008789241?*I, 
 [(1, 0.?e-57 - 1.414213562373095?*I, -1.000000000000000? + 1.414213562373095?*I)], 1)]

In other words, the product $A_1A_2A_3$ has eigenvalue $\lambda = 1$ with eigenvector $\vec{v} = (1,2,3)$ . This means that in the case that student 1 has 3 ounces of water, student 2 has 2 ounces of water, student 3 has 1 ounce of water, if each student shares water as explained in the problem, at the end of the process, they will each have the same amount as they started with.

I don’t know the answer to Q2 or Q3.

SymPy and the GSoC

Posted on 2012/04/23 by wdjoyner

Google has announced the results for Google Summer of Code. The following projects have been accepted for SymPy:

(Project, Student, Mentor, Link to proposal on the wiki)
– Category Theory Module, Sergiu Ivanov, Tom Bachmann
– Density Operators for Quantum Module in sympy.physics.quantum, Guru
Devanla, Brian Granger (co-mentor Sean Vig)
– Enhancements to sympy.physics.mechanics, Angadh Nanjangud, Gilbert Gede
– Group Theory, Aleksandar Makelov, David Joyner (Aaron Meurer co-mentor)
– Implicit Plotting Module, Bharath M R, Aaron Meurer
– Vector Analysis, Stefan Krastanov, Matthew Rocklin

I will help mentor Aleksandar Makelov’s work on group theory. He is a freshman at Harvard.

Bifid cipher and Sage

Posted on 2012/01/04 by wdjoyner

The Bifid cipher was invented around 1901 by Felix Delastelle. It is a “fractional substitution” cipher, where letters are replaced by pairs of symbols from a smaller alphabet. The cipher uses a 5×5 square filled with some ordering of the alphabet, except that “i”‘s and “j”‘s are identified (this is a so-called Polybius square; there is a 6×6 analog if you add back in “j” and also append onto the usual 26 letter alphabet, the digits 0, 1, …, 9). According to Helen Gaines’ book “Cryptanalysis”, this type of cipher was used in the field by the German army during World War I.

The Bifid cipher was discusses in Alasdair McAndrew’s book on Cryptography and Sage. We shall follow his discussion. As an example of a Polybius square for the Bifid cipher, pick the key to be “encrypt” (as Alasdair does). In that case, the Polybius square is $\left(\begin{array}{rrrrr} E & N & C & R & Y \\ P & T & A & B & C \\ D & E & F & G & H \\ I & K & L & M & N \\ O & P & Q & R & S \end{array}\right).$ BTW, the $6\times 6$ analog is: $\left(\begin{array}{rrrrrr} E & N & C & R & Y & P \\ T & A & B & C & D & E \\ F & G & H & I & J & K \\ L & M & N & O & P & Q \\ R & S & T & U & V & W \\ X & Y & Z & 0 & 1 & 2 \end{array}\right)$ .

Here is Sage code to produce the $6\times 6$ case (the $5\times 5$ case is in Alasdair’s book):

def bifid(pt, key):
    """
    INPUT:
        pt - plaintext string      (digits okay)
        key - short string for key (no repetitions, digits okay)
    
    OUTPUT:
        ciphertext from Bifid cipher (all caps, no spaces)

    This is the version of the Bifid cipher that uses the 6x6
    Polybius square.

    AUTHOR:
        Alasdair McAndrew

    EXAMPLES:
        sage: key = "encrypt"
        sage: pt = "meet me on monday at 8am"
        sage: bifid(pt, key)
        [[2, 5], [0, 0], [0, 0], [1, 0], [2, 5], [0, 0], [3, 0], 
         [0, 1], [2, 5], [3, 0], [0, 1], [1, 3], [1, 1], [0, 4], 
         [1, 1], [1, 0], [5, 4], [1, 1], [2, 5]]
        'HNHOKNTA5MEPEGNQZYG'

    """
    AS = AlphabeticStrings()
    A = list(AS.alphabet())+[str(x) for x in range(10)]
    # first make sure the letters are capitalized
    # and text has no spaces
    key0 = [x.capitalize() for x in key if not(x.isspace())]
    pt0 = [x.capitalize() for x in pt if not(x.isspace())]
    # create long key
    long_key = key0+[x for x in A if not(x in key0)]
    n = len(pt0)
    # the fractionalization
    pairs = [[long_key.index(x)//6, long_key.index(x)%6] for x in pt0]
    print pairs
    tmp_cipher = flatten([x[0] for x in pairs]+[x[1] for x in pairs])
    ct = join([long_key[6*tmp_cipher[2*i]+tmp_cipher[2*i+1]] for i in range(n)], sep="")
    return ct

def bifid_square(key):
    """
    Produced the Polybius square for the 6x6 Bifid cipher.

    EXAMPLE:
        sage: key = "encrypt"
        sage: bifid_square(key)
        [E N C R Y P]
        [T A B C D E]
        [F G H I J K]
        [L M N O P Q]
        [R S T U V W]
        [X Y Z 0 1 2]

    """
    AS = AlphabeticStrings()
    A = list(AS.alphabet())+[str(x) for x in range(10)]
    # first make sure the letters are capitalized
    # and text has no spaces
    key0 = [SR(x.capitalize()) for x in key if not(x.isspace())]
    # create long key
    long_key = key0+[SR(x) for x in A if not(x in key0)]
    # the fractionalization
    pairs = [[long_key.index(SR(x))//6, long_key.index(SR(x))%6] for x in A]
    f = lambda i,j: long_key[6*i+j] 
    M = matrix(SR, 6, 6, f)
    return M

Have fun!

God’s number for the Rubik’s cube in the face turn metric

Posted on 2010/08/29 by wdjoyner

This is an update to the older post.

The story is described well at cube20.org but the bottom line is that Tom Rokicki, Herbert Kociemba, Morley Davidson, and John Dethridge have established that every cube position can be solved in 20 face moves or less. The superflip is known to take 20 moves exactly (in 1995, Michael Reid established this), so 20 is best possible – or God’s number in the face turn algorithm. Google (and, earlier, Sony) contributed computer resources to do the needed massive computations. Congradulations to everyone who worked on this!

This would make a great documentary I think!

uniform matroids and MDS codes

Posted on 2010/03/10 by wdjoyner

It is known that uniform (resp. paving) matroids correspond to MDS (resp. “almost MDS” codes). This post explains this connection.

An MDS code is an $[n,k,d]$ linear error correcting block code $C$ which meets the Singleton bound, $d+k=n+1$ . A uniform matroid is a matroid for which all circuits are of size $\geq r(M)+1$ , where $r(M)$ is the rank of M. Recall, a circuit in a matroid M=(E,J) is a minimal dependent subset of E — that is, a dependent set whose proper subsets are all independent (i.e., all in J).

Consider a linear code $C$ whose check matrix is an $(n-k)\times n$ matrix $H=(\vec{h}_1,\dots , \vec{h}_n)$ . The vector matroid M=M[H] is a matroid for which the smallest sized dependency relation among the columns of H is determined by the check relations $c_1\vec{h}_1 + \dots + c_n \vec{h}_n = H\vec{c}=\vec{0}$ , where $\vec{c}=(c_1,\dots,c_n)$ is a codeword (in C which has minimum dimension d). Such a minimum dependency relation of H corresponds to a circuit of M=M[H].

Floyd-Warshall-Roy, 3

Posted on 2010/03/02 by wdjoyner

As in the previous post, let $G=(V,E)$ be a weighted digraph having $n$ vertices and let $A=A_G$ denote its $n\times n$ adjacency matrix. We identify the vertices with the set $\{1,2,\dots, n\}$ .

The previous post discussed the following result, due to Sturmfels et al.

Theorem: The entry of the matrix A^n-1 in row i and column j equals the length of a shortest path from vertex i to vertex j in G. (Here A^n-1 denotes the n-1-st tropical power of A.)

This post discusses an implementation in Python/Sage.

Consider the following class definition.

class TropicalNumbers:
    """
    Implements the tropical semiring.

    EXAMPLES:
        sage: T = TropicalNumbers()
        sage: print T
        Tropical Semiring

    """
    def __init__(self):
        self.identity = Infinity

    def __repr__(self):
        """
        Called to compute the "official" string representation of an object.
        If at all possible, this should look like a valid Python expression
        that could be used to recreate an object with the same value.

        EXAMPLES:
            sage: TropicalNumbers()
            TropicalNumbers()

        """
        return "TropicalNumbers()"

    def __str__(self):
        """
        Called to compute the "informal" string description of an object. 

        EXAMPLES:
            sage: T = TropicalNumbers()
            sage: print T
            Tropical Semiring

        """
        return "Tropical Semiring"

    def __call__(self, a):
        """
        Coerces a into the tropical semiring.

        EXAMPLES:
            sage: T(10)
            TropicalNumber(10)
            sage: print T(10)
            Tropical element 10 in Tropical Semiring
        """
        return TropicalNumber(a)

    def __contains__(self, a):
        """
        Implements "in".

        EXAMPLES:
            sage: T = TropicalNumbers()
            sage: a = T(10)
            sage: a in T
            True

        """
        if a in RR or a == Infinity:
            return a==Infinity or (RR(a) in RR)
        else:
            return a==Infinity or (RR(a.element) in RR)

class TropicalNumber:
    def __init__(self, a):
        self.element = a
        self.base_ring = TropicalNumbers()

    def __repr__(self):
        """
        Called to compute the "official" string representation of an object.
        If at all possible, this should look like a valid Python expression
        that could be used to recreate an object with the same value.

        EXAMPLES:

        """
        return "TropicalNumber(%s)"%self.element

    def __str__(self):
        """
        Called to compute the "informal" string description of an object. 

        EXAMPLES:
            sage: T = TropicalNumbers()
            sage: print T(10)
            Tropical element 10 in Tropical Semiring
        """
        return "%s"%(self.number())

    def number(self):
        return self.element

    def __add__(self, other):
        """
        Implements +. Assumes both self and other are instances of
        TropicalNumber class.

        EXAMPLES:
            sage: T = TropicalNumbers()
            sage: a = T(10)
            sage: a in T
            True
            sage: b = T(15)
            sage: a+b
            10

        """
        T = TropicalNumbers()
        return T(min(self.element,other.element))

    def __mul__(self, other):
        """
        Implements multiplication *.

        EXAMPLES:
            sage: T = TropicalNumbers()
            sage: a = T(10)
            sage: a in T
            True
            sage: b = T(15)
            sage: a*b
            25
        """
        T = TropicalNumbers()
        return T(self.element+other.element)

class TropicalMatrix:
    def __init__(self, A):
        T = TropicalNumbers()
        self.base_ring = T
        self.row_dimen = len(A)
        self.column_dimen = len(A[0])
        # now we coerce the entries into T
        A0 = A
        m = self.row_dimen
        n = self.column_dimen
        for i in range(m):
            for j in range(n):
                A0[i][j] = T(A[i][j])
        self.array = A0

    def matrix(self):
        """
        Returns the entries (as ordinary numbers).

        EXAMPLES:
            sage: A = [[0,1,3,7],[2,0,1,3],[4,5,0,1],[6,3,1,0]]
            sage: AT = TropicalMatrix(A)
            sage: AT.matrix()
            [[0, 1, 3, 7], [2, 0, 1, 3], [4, 5, 0, 1], [6, 3, 1, 0]]
        """
        m = self.row_dim()
        n = self.column_dim()
        A0 = [[0 for i in range(n)] for j in range(m)]
        for i in range(m):
            for j in range(n):
                A0[i][j] = (self.array[i][j]).number()
        return A0

    def row_dim(self):
        return self.row_dimen

    def column_dim(self):
        return self.column_dimen

    def __repr__(self):
        """
        Called to compute the "official" string representation of an object.
        If at all possible, this should look like a valid Python expression
        that could be used to recreate an object with the same value.

        EXAMPLES:

        """
        return "TropicalMatrix(%s)"%self.array

    def __str__(self):
        """
        Called to compute the "informal" string description of an object. 

        EXAMPLES:

        """
        return "Tropical matrix %s"%(self.matrix())

    def __add__(self, other):
        """
        Implements +. Assumes both self and other are instances of
        TropicalMatrix class.

        EXAMPLES:
            sage: A = [[1,2,Infinity],[3,Infinity,0]]
            sage: B = [[2,Infinity,1],[3,-1,1]]
            sage: AT = TropicalMatrix(A)
            sage: BT = TropicalMatrix(B)
            sage: AT
            TropicalMatrix([[TropicalNumber(1), TropicalNumber(2), TropicalNumber(+Infinity)],
              [TropicalNumber(3), TropicalNumber(+Infinity), TropicalNumber(0)]])
            sage: AT+BT
            [[TropicalNumber(1), TropicalNumber(2), TropicalNumber(1)],
             [TropicalNumber(3), TropicalNumber(-1), TropicalNumber(0)]]
        """
        A = self.array
        B = other.array
        C = []
        m = self.row_dim()
        n = self.column_dim()
        if m != other.row_dim:
            raise ValueError, "Row dimensions must be equal."
        if n != other.column_dim:
            raise ValueError, "Column dimensions must be equal."
        for i in range(m):
            row = [A[i][j]+B[i][j] for j in range(n)] # + as tropical numbers
            C.append(row)
        return C

    def __mul__(self, other):
        """
        Implements multiplication *.

        EXAMPLES:
            sage: A = [[1,2,Infinity],[3,Infinity,0]]
            sage: AT = TropicalMatrix(A)
            sage: B = [[2,Infinity],[-1,1],[Infinity,0]]
            sage: BT = TropicalMatrix(B)
            sage: AT*BT
             [[TropicalNumber(1), TropicalNumber(3)],
              [TropicalNumber(5), TropicalNumber(0)]]
            sage: A = [[0,1,3,7],[2,0,1,3],[4,5,0,1],[6,3,1,0]]
            sage: AT = TropicalMatrix(A)
            sage: A = [[0,1,3,7],[2,0,1,3],[4,5,0,1],[6,3,1,0]]
            sage: AT = TropicalMatrix(A)
            sage: print AT*AT*AT
            Tropical matrix [[0, 1, 2, 3], [2, 0, 1, 2], [4, 4, 0, 1], [5, 3, 1, 0]]
        """
        T = TropicalNumbers()
        A = self.matrix()
        B = other.matrix()
        C = []
        mA = self.row_dim()
        nA = self.column_dim()
        mB = other.row_dim()
        nB = other.column_dim()
        if nA != mB:
            raise ValueError, "Column dimension of A and row dimension of B must be equal."
        for i in range(mA):
            row = []
            for j in range(nB):
                c = T(Infinity)
                for k in range(nA):
                    c = c+T(A[i][k])*T(B[k][j])
                row.append(c.number())
            C.append(row)
        return TropicalMatrix(C)

This shows that the shortest distances of digraph with adjacency matrix $\begin{pmatrix} 0&1&3&7\\ 2&0&1&3\\ 4&5&0&1\\ 6&3&1&0\end{pmatrix},$ is equal to A³, which is equal to $\begin{pmatrix} 0&1&2&3\\ 2&0&1&2\\ 4&4&0&1\\ 5&3&1&0\end{pmatrix}.$ This verifies an example given in chapter 1 of the book by Maclagan and Sturmfels, Introduction to Tropical Geometry .

Sage and the future of mathematics

Posted on 2010/02/04 by wdjoyner

I am not a biologist nor a chemist, and maybe neither are you, but suppose we were. Now, if I described a procedure, in “standard” detail, to produce a result XYZ, you would (based on your reasonably expected expertise in the field), follow the steps you believe were described and either reproduce XYZ or, if I was mistaken, not be able to reproduce XYZ. This is called scientific reproducibility. It is cructial to what I believe is one of the fundamental principles of science, namely Popper’s Falsifiability Criterion.

More and more people are arguing, correctly in my opinion, that in the computational realm, in particular in mathematical research which uses computational experiments, that much higher standards are needed. The Ars Technica article linked above suggests that “it’s time for a major revision of the scientific method.” Also, Victoria Stodden argues one must “facilitate reproducibility. Without this data may be open, but will remain de facto in the ivory tower.” The argument basically is that to reproduce computational mathematical experiments is unreasonably difficult, requiring more that a reasonable expertise. These days, it may in fact (unfortunately) require purchasing very expensive software, or possessing of very sophisticated programming skills, accessibility to special hardware, or (worse) guessing parameters and programming procedures only hinted at by the researcher.

Hopefully, Sage can play the role of a standard bearer for such computational reproducibility. Sage is free, open source and there is a publically available server it runs on (sagenb.org).

What government agencies should require such reproducibility? In my opinion, all scientific funding agencies (NSF, etc) should follow these higher standards of computational accountability.

Yet Another Mathblog

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