Metric Dimension of Hamming Graphs and Applications to

Metric Dimension of Hamming Graphs and Applicationsto Computational Biology

Lucas Laird

Department of Computer Science and Department of Applied MathUniversity of Colorado, Boulder

July 1, 2020

Lucas Laird (CU-Boulder) Resolvability of Hamming Graphs July 1, 2020 1 / 37

Introduction

K-mer: A string of length k with symbols chosen from a referencealphabet.

Examples:

Genetic sequences: A,C,T,G base pairs

Amino Acid sequences: 20 different amino acids

Phone numbers: digits

The Hamming distance between two k-mers, denoted d(·, ·), is thenumber of positions where they differ.

Example: d(ABBA,ACBC ) = 2 since they differ at two positions.

Introduction

Examples:

Introduction

Examples:

Introduction

Examples:

IntroductionHamming graph

A Hamming graph Hk,a has a vertex set of all k-mers made from analphabet of size a. Two vertices v1, v2 are neighbors if d(v1, v2) = 1. Theshortest path distance between any two vertices is their Hamming distance.

Example:

Figure: Hamming graph Hk=4,a=2

IntroductionHamming graph

A Hamming graph Hk,a has a vertex set of all k-mers made from analphabet of size a. Two vertices v1, v2 are neighbors if d(v1, v2) = 1. Theshortest path distance between any two vertices is their Hamming distance.

Example:

Figure: Hamming graph Hk=4,a=2

Metric Dimension and Resolving Sets

Definition (Resolving Set [1, 2])

Let G = (V ,E ) be a connected simple graph and d(x , y) be the shortestpath distance between vertices x , y in G . A set R ⊆ V is resolving if forevery pair of distinct vertices v1, v2 ∈ V there is an r ∈ R such thatd(v1, r) 6= d(v2, r).

The metric dimension, β(G ), is the size of a smallest possible resolvingset.

Metric Dimension and Resolving Sets

Definition (Resolving Set [1, 2])

Let G = (V ,E ) be a connected simple graph and d(x , y) be the shortestpath distance between vertices x , y in G . A set R ⊆ V is resolving if forevery pair of distinct vertices v1, v2 ∈ V there is an r ∈ R such thatd(v1, r) 6= d(v2, r).

The metric dimension, β(G ), is the size of a smallest possible resolvingset.

Resolving Set Embeddings

The vertices of a graph can be embedded by using a resolving set.

Example:

Figure: R = {2, 3} is a minimal resolving set

The size of the resolving set is the dimension of the embedding.

Example:

Motivating Example

What if we wanted to check if any nodes in this resolving set on H8,20 [3]can be removed so that it stays resolving?

AAAAAAAA, AAAAAAAR, AAAAAARA, AAAAARAA, AAAARAAA, AAARAAAA,ARWAAAAA, CCCHHHHH, CCCHHHHI , CCCHHHIA, CCCHHIAA, CCCHIAAA,CCCIAAAA, CNSAAAAA, DDDEEEEE , DDDEEEEG , DDDEEEGA, DDDEEGAA,DDDEGAAA, DDDGAAAA, DHFAAAAA, EAGAAAAA, EEEFAAAA, EEEMFAAA,EEEMMFAA, EEEMMMFA, EEEMMMMF , EEEMMMMM, FFFAAAAA, GGGPPPPP,GGGPPPPS , GGGPPPSA, GGGPPSAA, GGGPSAAA, GGGSAAAA, HHHTTTTT ,HHHTTTTW , HHHTTTWA, HHHTTWAA, HHHTWAAA, HHHWAAAA, HPVAAAAA,IIIVAAAA, IIIYVAAA, IIIYYVAA, IIIYYYVA, IIIYYYYV , IIIYYYYY ,

KKKAAAAA, KLQAAAAA, LLLAAAAA, MKYAAAAA, MMMMAAAA, NNNCCCCC ,NNNCCCCQ, NNNCCCQA, NNNCCQAA, NNNCQAAA, NNNQAAAA, NSTAAAAA,PPPAAAAA, QPKAAAAA, QQQKAAAA, QQQLKAAA, QQQLLKAA, QQQLLLKA,QQQLLLLK , QQQLLLLL, QYEAAAAA, RRRDAAAA, RRRNDAAA, RRRNNDAA,RRRNNNDA, RRRNNNND, RRRNNNNN, SISAAAAA, SVTAAAAA, TTCAAAAA,VFRAAAAA, WMPAAAAA, WWDAAAAA, YGLAAAAA

For 25.6 billion vertices in H8,20, around 600 quintillion pairwisechecks per node would be required.

Motivating Example

What if we wanted to check if any nodes in this resolving set on H8,20 [3]can be removed so that it stays resolving?

AAAAAAAA, AAAAAAAR, AAAAAARA, AAAAARAA, AAAARAAA, AAARAAAA,ARWAAAAA, CCCHHHHH, CCCHHHHI , CCCHHHIA, CCCHHIAA, CCCHIAAA,CCCIAAAA, CNSAAAAA, DDDEEEEE , DDDEEEEG , DDDEEEGA, DDDEEGAA,DDDEGAAA, DDDGAAAA, DHFAAAAA, EAGAAAAA, EEEFAAAA, EEEMFAAA,EEEMMFAA, EEEMMMFA, EEEMMMMF , EEEMMMMM, FFFAAAAA, GGGPPPPP,GGGPPPPS , GGGPPPSA, GGGPPSAA, GGGPSAAA, GGGSAAAA, HHHTTTTT ,HHHTTTTW , HHHTTTWA, HHHTTWAA, HHHTWAAA, HHHWAAAA, HPVAAAAA,IIIVAAAA, IIIYVAAA, IIIYYVAA, IIIYYYVA, IIIYYYYV , IIIYYYYY ,

KKKAAAAA, KLQAAAAA, LLLAAAAA, MKYAAAAA, MMMMAAAA, NNNCCCCC ,NNNCCCCQ, NNNCCCQA, NNNCCQAA, NNNCQAAA, NNNQAAAA, NSTAAAAA,PPPAAAAA, QPKAAAAA, QQQKAAAA, QQQLKAAA, QQQLLKAA, QQQLLLKA,QQQLLLLK , QQQLLLLL, QYEAAAAA, RRRDAAAA, RRRNDAAA, RRRNNDAA,RRRNNNDA, RRRNNNND, RRRNNNNN, SISAAAAA, SVTAAAAA, TTCAAAAA,VFRAAAAA, WMPAAAAA, WWDAAAAA, YGLAAAAA

For 25.6 billion vertices in H8,20, around 600 quintillion pairwisechecks per node would be required.

One-Hot Encodings

Definition (One-Hot encoding)

The one-hot encoding of a vertex v in Hk,a, is an a× k binary matrix Vwhere Vj ,i = 1 if j = v [i ], otherwise Vj ,i = 0.

Example:

The one-hot encoding of 1213 in H4,3 is the matrix

1 0 1 00 1 0 00 0 0 1

One-Hot Encodings

Definition (One-Hot encoding)

The one-hot encoding of a vertex v in Hk,a, is an a× k binary matrix Vwhere Vj ,i = 1 if j = v [i ], otherwise Vj ,i = 0.

Example:

The one-hot encoding of 1213 in H4,3 is the matrix

1 0 1 00 1 0 00 0 0 1

Computing Hamming distance with One-Hot encodings

Theorem (Laird, Tillquist, Lladser)

For any two vertices a and b in Hk,a, if A and B denote their one-hotencodings, respectively, then:

d(a, b) = k − Tr(ATB)

Example: v1 = 1223, v2 = 2123

Tr(V T1 V2) = Tr(

1 0 00 1 00 1 00 0 1

0 1 0 0

1 0 1 00 0 0 1

) = Tr(

0 1 0 01 0 1 01 0 1 00 0 0 1

Example: v1 = 1223, v2 = 2123

Tr(V T1 V2) = Tr(

1 0 00 1 00 1 00 0 1

0 1 0 0

1 0 1 00 0 0 1

) = Tr(

0 1 0 01 0 1 01 0 1 00 0 0 1

Example: v1 = 1223, v2 = 2123

Tr(V T1 V2) = Tr(

1 0 00 1 00 1 00 0 1

0 1 0 0

1 0 1 00 0 0 1

) = Tr(

0 1 0 01 0 1 01 0 1 00 0 0 1

Applying the Theorem to Resolvability

Let R be a subset of vertices in Hk,a, and V1, ...,Vn be the columnvectorized one-hot encodings of the vertices in R. Define:

V T1...

For x , y ∈ Hk,a with X ,Y column vectorized one-hot encodings:

A(X − Y ) =

d(r1, y)− d(r1, x)...

d(rn, y)− d(rn, x)

Therefore, x , y are resolved by R iff A(X − Y ) 6= 0.

Applying the Theorem to Resolvability

Let R be a subset of vertices in Hk,a, and V1, ...,Vn be the columnvectorized one-hot encodings of the vertices in R. Define:

V T1...

For x , y ∈ Hk,a with X ,Y column vectorized one-hot encodings:

A(X − Y ) =

d(r1, y)− d(r1, x)...

d(rn, y)− d(rn, x)

Therefore, x , y are resolved by R iff A(X − Y ) 6= 0.

Main Result

Let R = {r1, . . . , rn} be a subset of vertices in Hk,a of cardinality n. If foreach 1 ≤ i ≤ n, Vi denotes the column vectorized one-hot encoding of ri ,and we define the matrix

V T1...

then R is resolving iff there exists no non-trivial solution z to thesystem Az = 0 satisfying the following constraints: if z is decomposedinto k blocks of length a as follows:(

(z1, . . . , za), (za+1, . . . , z2a), ..., (z(k−1)a+1, . . . , zka))T,

then each block is identically zero, or it has exactly one 1 and one −1entry and all other entries are 0.

Example of Theorem

Consider the resolving set R = {100, 101, 001} on H3,2.

0 1 1 0 1 00 1 1 0 0 11 0 1 0 0 1

, so: rref(A) =

1 0 1 0 0 10 1 1 0 0 10 0 0 0 1 −1

With z =

((z1, z2), (z3, z4), (z5, z6)

), rref(A) gives:

z1 = −z3 − z6; z2 = −z3 − z6; z5 = z6.

Going through constraints on z3, z4, z6 will give the values for z1, z2, z5.

1 z5 = z6 implies z5 = z6 = 0 since z5, z6 are in the same block.

2 z1 = z2 = −z3 so z1 = z2 = 0 since z1, z2 are in the same block.

3 z3 = 0 implies z4 = 0 since z3, z4 are in the same block.

Therefore only the trivial solution z = 0 exists and R is resolving on H3,2.

Example of Theorem

0 1 1 0 1 00 1 1 0 0 11 0 1 0 0 1

, so: rref(A) =

1 0 1 0 0 10 1 1 0 0 10 0 0 0 1 −1

With z =((z1, z2), (z3, z4), (z5, z6)

), rref(A) gives:

z1 = −z3 − z6; z2 = −z3 − z6; z5 = z6.

Example of Theorem

0 1 1 0 1 00 1 1 0 0 11 0 1 0 0 1

, so: rref(A) =

1 0 1 0 0 10 1 1 0 0 10 0 0 0 1 −1

With z =

((z1, z2), (z3, z4), (z5, z6)

), rref(A) gives:

z1 = −z3 − z6; z2 = −z3 − z6; z5 = z6.

Example of Theorem

0 1 1 0 1 00 1 1 0 0 11 0 1 0 0 1

, so: rref(A) =

1 0 1 0 0 10 1 1 0 0 10 0 0 0 1 −1

With z =

((z1, z2), (z3, z4), (z5, z6)

), rref(A) gives:

z1 = −z3 − z6; z2 = −z3 − z6; z5 = z6.

Automatically Handling Constraints

z1(z1 − 1)(z1 + 1) = 0...

zak(zak − 1)(zak + 1) = 0

a∑i=1

zi = 0

...ka∑

(k−1)a+1

zi = 0

i=1z2i )(2−

a∑i=1

z2i ) = 0

(ka∑

i=(k−1)a+1

z2i )(2−ka∑

i=(k−1)a+1

z2i ) = 0

Resolvability is equivalent to showing {P = 0} ∩ ker(A) is only thetrivial solution z = 0.

z1(z1 − 1)(z1 + 1) = 0...

zak(zak − 1)(zak + 1) = 0a∑

i=1zi = 0

...ka∑

(k−1)a+1

zi = 0

i=1z2i )(2−

a∑i=1

z2i ) = 0

(ka∑

i=(k−1)a+1

z2i )(2−ka∑

i=(k−1)a+1

z2i ) = 0

z1(z1 − 1)(z1 + 1) = 0...

zak(zak − 1)(zak + 1) = 0a∑

i=1zi = 0

...ka∑

(k−1)a+1

zi = 0

i=1z2i )(2−

a∑i=1

z2i ) = 0

(ka∑

i=(k−1)a+1

z2i )(2−ka∑

i=(k−1)a+1

z2i ) = 0

z1(z1 − 1)(z1 + 1) = 0...

zak(zak − 1)(zak + 1) = 0a∑

i=1zi = 0

...ka∑

(k−1)a+1

zi = 0

i=1z2i )(2−

a∑i=1

z2i ) = 0

(ka∑

i=(k−1)a+1

z2i )(2−ka∑

i=(k−1)a+1

z2i ) = 0

Solving the Polynomial System

Definition ([4, 5])

For a set of polynomials in the variable z = (z1, . . . , zk),P = {p1(z), . . . , pn(z)}, the associated polynomial ideal, denoted I (P),is defined as the set of all polynomials of the form f1p1 + . . .+ fnpn, wherefi (z) are arbitrary polynomials.

z is a solution of P = 0 iff, for each g ∈ I (P), g(z) = 0.

It is often more convenient to characterize the roots of I (P) insteadof P itself.

Grobner bases can be intuitively understood as orthonormal bases forpolynomial ideals of the form I (P).

Definition ([4, 5])

Important Definitions for Grobner Bases

Definition ([4, 5])

A monomial in z is defined as any product of the form za11 · · · zakk , with

non-negative integers a1, . . . , ak . This product is often written as za, witha = (a1, . . . , ak).

For what follows, we must define an ordering on monomials.Lexicographic Ordering: za > zb if

There is an index i such that ai > bi and aj = bj for all j < iExample:

z1z24 > z2z

z32 z4 > z2z34

Definition ([4, 5])

A monomial in z is defined as any product of the form za11 · · · zakk , with

non-negative integers a1, . . . , ak . This product is often written as za, witha = (a1, . . . , ak).

For what follows, we must define an ordering on monomials.Lexicographic Ordering: za > zb if

There is an index i such that ai > bi and aj = bj for all j < iExample:

z1z24 > z2z

z32 z4 > z2z34

Order the terms of a polynomial p in descending monomial order,p = αza + βzb + . . .; in particular, za > zb.

Leading Term: LT (p) := αza

Leading Monomial: LM(p) := za

Leading Coefficient: LC (p) := α

Definition ([4, 5])

The S-Polynomial of two polynomials p1, p2, denoted spoly(p1, p2), is

spoly(p1, p2) :=LCM(LM(p1), LM(p2))

LT (p1)p1 −

LCM(LM(p1), LM(p2))

LT (p2)p2

Definition ([4, 5])

LT (p1)p1 −

LCM(LM(p1), LM(p2))

LT (p2)p2

Definition ([4, 5])

LT (p1)p1 −

LCM(LM(p1), LM(p2))

LT (p2)p2

Polynomial Reduction

Definition ([4, 5])

For a set of polynomials P = {p1, . . . , pn} and a monomial ordering >,any polynomial f can be written:

f = q1p1 + . . .+ qnpn + r ,

where r is such that no monomial in r is divisible by any LM(pi ).

This is called reducing f by P, denoted fP→ r . qi are called quotients

and r is called the remainder or reduction.

The reduction fP→ r is not unique in general since the order in which the

polynomials in P are processed matters.

Polynomial Reduction

Definition ([4, 5])

For a set of polynomials P = {p1, . . . , pn} and a monomial ordering >,any polynomial f can be written:

f = q1p1 + . . .+ qnpn + r ,

where r is such that no monomial in r is divisible by any LM(pi ).

This is called reducing f by P, denoted fP→ r . qi are called quotients

and r is called the remainder or reduction.

The reduction fP→ r is not unique in general since the order in which the

polynomials in P are processed matters.

Buchberger’s Criterion

Definition

(Buchberger’s Criterion.) A set of polynomials G = {g1, . . . , gn} is aGrobner basis for a polynomial ideal I if and only if I (G ) = I and for allpairs gi , gj ∈ G :

Spoly(gi , gj)G→ 0.

A Grobner basis G is called reduced if for all distinct gi , gj ∈ G ,LC (gi ) = 1, and no monomial of gj is divisible by LT (gi ).

Under a given monomial ordering, the reduced Grobner basis isunique.

For a Grobner basis G and polynomial f , fG→ 0 iff f ∈ I (G ).

Buchberger’s Criterion

Definition

(Buchberger’s Criterion.) A set of polynomials G = {g1, . . . , gn} is aGrobner basis for a polynomial ideal I if and only if I (G ) = I and for allpairs gi , gj ∈ G :

Spoly(gi , gj)G→ 0.

A Grobner basis G is called reduced if for all distinct gi , gj ∈ G ,LC (gi ) = 1, and no monomial of gj is divisible by LT (gi ).

Under a given monomial ordering, the reduced Grobner basis isunique.

For a Grobner basis G and polynomial f , fG→ 0 iff f ∈ I (G ).

What were we doing again?

V T1...

Az = 0

z1(z1 − 1)(z1 + 1) = 0...

zak(zak − 1)(zak + 1) = 0∑ai=1 zi = 0

...∑ka(k−1)a+1 zi = 0

i=1 z2i )(2−

∑ai=1 z

2i ) = 0

(∑ka

i=(k−1)a+1 z2i )(2−

∑kai=(k−1)a+1 z

2i ) = 0

What were we doing again?

V T1...

Az = 0

z1(z1 − 1)(z1 + 1) = 0...

zak(zak − 1)(zak + 1) = 0∑ai=1 zi = 0

...∑ka(k−1)a+1 zi = 0

i=1 z2i )(2−

∑ai=1 z

2i ) = 0

(∑ka

i=(k−1)a+1 z2i )(2−

∑kai=(k−1)a+1 z

2i ) = 0

Hilbert’s Weak Nullstellensatz

Theorem (Hilbert’s Weak Nullstellensatz [4, 6])

Let P = {p1, ..., pk} be a set of polynomials with reduced Grobner basisG . The solution set {P = 0} = ∅ if and only if G = {1}.

We need to check if the system P ∪ Az = 0 has any non-trivialsolutions to show whether R is resolving on Hk,a.

Define fi =ak∑j=1

z2j − 2i = 0, and Pi = P ∪ fi for i = 1, . . . , k .

Pi ∪ Az = 0 will have only non-trivial solutions.

Let Gi be the reduced Grobner basis of Pi ∪ Az . R is resolving iffGi = {1} for all i = 1, . . . , k .

Define fi =ak∑j=1

Leveraging the Structure of P

We can partition P into disjoint sets (which we call blocks) as follows.For each i = 0, . . . , (k − 1), define:

zia+1(zia+1 − 1)(zia+1 + 1)...

z(i+1)a(z(i+1)a − 1)(z(i+1)a + 1)(i+1)a∑j=ia+1

((i+1)a∑j=ia+1

z2j )(2−(i+1)a∑j=ia+1

Then P = P0 ∪ . . . ∪ Pk−1.Let zi be the variables for the block Pi ; in particular, zi ∩ zj = ∅ for i 6= j .

Since the variables of different blocks are disjoint, we can use the following:

Lemma ([5])

Consider two polynomial sets P1 and P2 with disjoint variables x and y ,and reduced Grobner bases G1 6= {1} and G2 6= {1}, respectively. Then(G1 ∪ G2) is a reduced Grobner basis of (P1 ∪ P2).This extends via induction to any finite unions of polynomial sets that donot share variables.

Applying this lemma to P = P0 ∪ . . . ∪ Pk−1 gives:

Corollary (Laird, Tillquist, Lladser)

Let Gi be the reduced Grobner basis of Pi . Then G = (G0 ∪ . . . ∪ Gk−1) isthe reduced Grobner basis of P.

Since the variables of different blocks are disjoint, we can use the following:

Lemma ([5])

Consider two polynomial sets P1 and P2 with disjoint variables x and y ,and reduced Grobner bases G1 6= {1} and G2 6= {1}, respectively. Then(G1 ∪ G2) is a reduced Grobner basis of (P1 ∪ P2).This extends via induction to any finite unions of polynomial sets that donot share variables.

Applying this lemma to P = P0 ∪ . . . ∪ Pk−1 gives:

Let Gi be the reduced Grobner basis of Pi . Then G = (G0 ∪ . . . ∪ Gk−1) isthe reduced Grobner basis of P.

Every block Pi is equivalent to block P0 under the variable change:

zia+j −→ zj

zia+1(zia+1 − 1)(zia+1 + 1)...

z(i+1)a(z(i+1)a − 1)(z(i+1)a + 1)(i+1)a∑j=ia+1

((i+1)a∑j=ia+1

z2j )(2−(i+1)a∑j=ia+1

−→

z1(z1 − 1)(z1 + 1)...

za(za − 1)(za + 1)a∑

j=1z2j )(2−

a∑j=1

So it is only necessary to compute G0 since reversing the variable changeon G0 yields Gi .

zia+j −→ zj

zia+1(zia+1 − 1)(zia+1 + 1)...

z(i+1)a(z(i+1)a − 1)(z(i+1)a + 1)(i+1)a∑j=ia+1

((i+1)a∑j=ia+1

z2j )(2−(i+1)a∑j=ia+1

−→

z1(z1 − 1)(z1 + 1)...

za(za − 1)(za + 1)a∑

j=1z2j )(2−

a∑j=1

zia+j −→ zj

zia+1(zia+1 − 1)(zia+1 + 1)...

z(i+1)a(z(i+1)a − 1)(z(i+1)a + 1)(i+1)a∑j=ia+1

((i+1)a∑j=ia+1

z2j )(2−(i+1)a∑j=ia+1

−→

z1(z1 − 1)(z1 + 1)...

za(za − 1)(za + 1)a∑

j=1z2j )(2−

a∑j=1

Explicit Pattern Representation of G0

P0 only changes with the alphabet size a, and increasing k simply addsanother block to P. Studying how G0 changes with a will give insight intoall of G .

Used Python computeralgebra packageSymPy [7] to computeGrobner bases.

Studying the computedGrobner bases for smallvalues of a illuminatedan explicit pattern.

∑ai=1 zi

z2(z2 − 1)(z2 + 1)...

za(za − 1)(za + 1)

z2z3(z2 + z3)...

za−1za(za−1 + za)

z2z3z4...

za−2za−1za

∑ai=1 zi

z2(z2 − 1)(z2 + 1)...

za(za − 1)(za + 1)

z2z3(z2 + z3)...

z2z3z4...

za−2za−1za

∑ai=1 zi

z2(z2 − 1)(z2 + 1)...

za(za − 1)(za + 1)

z2z3(z2 + z3)...

z2z3z4...

za−2za−1za

Simplifying the System for Hypercubes

The general approach on Hk,a can be simplified for hypercubes, Hk,2.

Consider the first block of a polynomial system for Hk,2:

z1(z1 − 1)(z1 + 1) = 0z2(z2 − 1)(z2 + 1) = 0

z1 + z2 = 0

(z21 + z22 )(2− (z21 + z22 )) = 0

Because of the block structure of P, any simplifications we performon P0 will be valid for every other block.

The general approach on Hk,a can be simplified for hypercubes, Hk,2.

Consider the first block of a polynomial system for Hk,2:

z1(z1 − 1)(z1 + 1) = 0z2(z2 − 1)(z2 + 1) = 0

z1 + z2 = 0

(z21 + z22 )(2− (z21 + z22 )) = 0

Because of the block structure of P, any simplifications we performon P0 will be valid for every other block.

z1(z1 − 1)(z1 + 1) = 0z2(z2 − 1)(z2 + 1) = 0

z1 + z2 = 0

(z21 + z22 )(2− (z21 + z22 )) = 0

The third polynomial gives z1 = −z2 and z21 = z22 .

Substitution into the first and fourth polynomials gives:

z1(z1 − 1)(z1 + 1) = −(z32 − z2)

(z21 + z22 )(2− (z21 + z22 )) = −4z2(z32 − z2)

These redundancies lead to a simplified but equivalent polynomial system:

{z2(z2 − 1)(z2 + 1) = 0

z1 + z2 = 0

z1(z1 − 1)(z1 + 1) = 0z2(z2 − 1)(z2 + 1) = 0

z1 + z2 = 0

(z21 + z22 )(2− (z21 + z22 )) = 0

z1(z1 − 1)(z1 + 1) = −(z32 − z2)

(z21 + z22 )(2− (z21 + z22 )) = −4z2(z32 − z2)

{z2(z2 − 1)(z2 + 1) = 0

z1 + z2 = 0

z1(z1 − 1)(z1 + 1) = 0z2(z2 − 1)(z2 + 1) = 0

z1 + z2 = 0

(z21 + z22 )(2− (z21 + z22 )) = 0

z1(z1 − 1)(z1 + 1) = −(z32 − z2)

(z21 + z22 )(2− (z21 + z22 )) = −4z2(z32 − z2)

{z2(z2 − 1)(z2 + 1) = 0

z1 + z2 = 0

We can apply the same simplification to every block of P. Additionally,since zi−1 + zi = 0 are linear equations, they can be removed from thepolynomial system and transferred to the linear system.

Recall the matrix A from our main result, and let Ai be its i-th column:

A1 A2 . . . A2k−1 A2k

...z2k

Applying the linear functions zi−1 + zi = 0 results in a simplified system: (A2 − A1) . . . (A2k − A2k−1)

...z2k

We can apply the same simplification to every block of P. Additionally,since zi−1 + zi = 0 are linear equations, they can be removed from thepolynomial system and transferred to the linear system.

Recall the matrix A from our main result, and let Ai be its i-th column:

A1 A2 . . . A2k−1 A2k

...z2k

Applying the linear functions zi−1 + zi = 0 results in a simplified system: (A2 − A1) . . . (A2k − A2k−1)

...z2k

The simplifications, after renaming variables z2i −→ zi , give the following:

Let R = {v1, . . . , vn} be a set of nodes in Hk,2, and vi denote the logicalnegation (flip) of vi . Consider the matrix of dimensions (n× k) defined as:

v1 − v1...

vn − vn

Then, R resolves Hk,2 iff the equation Bz = 0, with z ∈ {−1, 0, 1}k , hasonly a trivial solution.

The corollary reproduces the system developed by A. F. Beardon [1]

Preliminary Runtime Analysis

We created 4,359 example sets on small Hamming graphs to test thetheory. Approximately 200 sets were generated for k = 1, . . . , 10 anda = 2, . . . , 5, with ak ≤ 25 to limit overall complexity.

Figure: Runtime for Grobner basis approach on resolving sets

Runtime Comparison

Figure: Runtime comparison on onlyresolving sets

Figure: Runtime comparison on resolvingand non-resolving sets

Grobner basis algorithm performance on non-resolving sets is onaverage longer than on resolving sets.Slow performance could be caused by the sets’ associated linearsystems or due to known, unresolved issues with SymPy.

Runtime Comparison

Current and Future Work

Study the block structure of the polynomial system to furtheroptimize Grobner basis computations.

Study structure of the Grobner bases Gi and try to derive a linearsystem which reduces them to {1}.

Explore if simplifications similar to the hypercubes exist on otheralphabet sizes.

Implement algorithms on H8,20.

Further computational complexity analysis.

Check how reducing the size of resolving sets impacts quality ofembeddings for machine learning algorithms.

Study structure of the Grobner bases Gi and try to derive a linearsystem which reduces them to {1}.Explore if simplifications similar to the hypercubes exist on otheralphabet sizes.

Acknowledgements

Professor Manuel Lladser (Advisor)

Richard Carter Tilquist (Co-Advisor)

Stephen Becker and Rafael Frongillo (Committee Members)

This research has been partially funded by NSF IIS grant 1836914

References I

[1] A. F. Beardon, “Resolving the Hypercube,” Discrete AppliedMathematics, vol. 161, pp. 1882–1887, 2013.

[2] J. Caceres, C. Hernando, M. Mora, I. M. Pelayo, M. L. Puertes,C. Seara, and D. R. Wood, “On the metric dimension of cartesianproducts of graphs,” Siam Journal of Discrete Math, vol. 21, no. 2,pp. 423–441, 2007.

[3] R. C. Tillquist and M. E. Lladser, “Low-dimensional representation ofgenomic sequences,” Journal of Mathematical Biology, pp. 1–29, 32019.

[4] D. Cox, J. Little, and D. O’Shea, Using Algebraic Geometry, vol. 1 ofGraduate Texts in Mathematics.Springer-Verlag New York, 1998.

[5] D. A. Cox, J. Little, and D. O’Shea, Grobner Bases, pp. 49–119.Cham: Springer International Publishing, 2015.

References II

[6] T. Tao, “Hilbert’s nullstellensatz,” November 2007.

[7] A. Meurer, C. P. Smith, M. Paprocki, O. Certık, S. B. Kirpichev,M. Rocklin, A. Kumar, S. Ivanov, J. K. Moore, S. Singh,T. Rathnayake, S. Vig, B. E. Granger, R. P. Muller, F. Bonazzi,H. Gupta, S. Vats, F. Johansson, F. Pedregosa, M. J. Curry, A. R.Terrel, v. Roucka, A. Saboo, I. Fernando, S. Kulal, R. Cimrman, andA. Scopatz, “Sympy: symbolic computing in python,” PeerJComputer Science, vol. 3, p. e103, Jan. 2017.

[8] M. Hauptmann, R. Schmied, and C. Viehmann, “Approximationcomplexity of metric dimension problem,” Journal of DiscreteAlgorithms, vol. 14, pp. 214–222, 2012.

[9] P. J. Slater, “Leaves of trees,” Congressus Numerantium, vol. 14,pp. 549–559, 1975.

References III

[10] F. Haray and R. A. Melter, “On the metric dimension of a graph,”Ars Combinatoria, vol. 2, pp. 191–195, 1976.

[11] V. Chvatal, “Mastermind,” Combinatorica, vol. 3, no. 3,pp. 325–329, 1983.

[12] C. Hernando, M. Mora, I. M. Pelayo, and C. Seara, “Extremal graphtheory for metric dimension and diameter,” Electronic Journal ofCombinatorics, vol. 17, no. R30, 2010.

[13] J. Caceres, D. Garijo, M. L. Puertas, and C. Seara, “On thedetermining number and the metric dimension of graphs,” ElectronicJournal of Combinatorics, vol. 17, no. R63, 2010.

[14] J. Kratica, V. Kovacevic-Vujcic, and M. Cangalovic, “Computing themetric dimension of graphs by genetic algorithms,” ComputationalOptimization and Applications, vol. 44, no. 2, p. 343, 2007.

References IV

[15] M. R. Garey and D. S. Johnson, Computers and Intractability: AGuide to the Theory of NP-Completeness.W. H. Freeman & Co., 1990.

[16] F. A. Chaouche and A. Berrachedi, “Automorphisms group ofgeneralized Hamming graphs,” Electronic Notes in DiscreteMathematics, vol. 24, no. 9-15, 2006.

[17] P. Erdos and A. Renyi, “On two problems of information theory,”Magyar Tud. Akad. Mat. Kutato Int. Kozl, vol. 8, pp. 229–243, 1963.

[18] B. Lindstrom, “On a combinatory detection problem i,” I. MagyarTud. Akad. Mat. Kutato Int. Kozl, vol. 9, pp. 195–207, 1964.

[19] J. C. Faugere, “A new efficient algorithm for computing Grobnerbases without reduction to zero (f5),” in Proceedings of the 2002International Symposium on Symbolic and Algebraic Computation,ISSAC ’02, (New York, NY, USA), pp. 75–83, ACM, 2002.

References V

[20] Y. Sun and D. Wang, “The F5 algorithm in Buchberger’s style,”CoRR, vol. abs/1006.5299, 2010.

[21] R. K. Guy and R. J. Nowakowski, “Coin-weighing problems,” TheAmerican Mathematical Monthly, vol. 102, no. 2, pp. 164–167, 1995.

[22] L. M. Kelly and E. A. Nordhaus, “Distance sets in metric spaces,”Trans. Amer. Math. Soc., vol. 71, pp. 440–456, 1951.

[23] R. A. Horn, R. A. Horn, and C. R. Johnson, Matrix analysis.Cambridge university press, 1990.

[24] S. Khuller, B. Raghavachari, and A. Rosenfeld, “Landmarks ingraphs,” Discrete Applied Mathematics, vol. 70, pp. 217–229, 1996.

[25] J.-C. Faugere, “A new efficient algorithm for computing Grobnerbases F4,” Journal of Pure and Applied Algebra, vol. 139, no. 1,pp. 61 – 88, 1999.

References VI

[26] C. Eder and J. E. Perry, “F5c: A variant of faugere’s f5 algorithmwith reduced grobner bases,” J. Symb. Comput., vol. 45, no. 12,pp. 1442–1458, 2010.MEGA’2009.

[27] A. Grover and J. Leskovec, “node2vec: Scalable feature learning fornetworks,” in Proceedings of the 22nd ACM SIGKDD InternationalConference on Knowledge Discovery and Data Mining, pp. 855–864,ACM, 2016.

[28] W. J. Krzanowski, Principles of Multivariate Analysis: A User’sPerspective.OUP Oxford, 2000.

[29] P. J. Olver and C. Shakiban, Linear Algebraic Systems, pp. 1–74.Cham: Springer International Publishing, 2018.

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