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Computations about formal multiple zeta spaces defined by binary extended double shuffle relations

Tomoya Machide National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan
Abstract

The formal multiple zeta space we consider with a computer is an 𝔽2\mathbb{F}_{2}-vector space generated by 2k22^{k-2} formal symbols for a given weight kk, where the symbols satisfy binary extended double shuffle relations. Up to weight k=22k=22, we compute the dimensions of the formal multiple zeta spaces, and verify the dimension conjecture on original extended double shuffle relations of real multiple zeta values. Our computations adopt Gaussian forward elimination and give information for spaces filtered by depth. We can observe that the dimensions of the depth-graded formal multiple zeta spaces have a Pascal triangle pattern expected by the Hoffman mult-indices.

000e-mail : machide@nii.ac.jp000MSC-class: 11M32 (Primary); 15A03,68W30 (Secondary)000Key words: multiple zeta value, graded vector space, dimension calculation

1 Introduction

The space generated by multiple zeta values (MZVs for short) has been elucidated theoretically and numerically in recent years, but its structure remains mysterious. In this paper, we shed light on a formal space generated by binary analogs of MZVs by computer experiments for unraveling both of the original and formal spaces.

Let \mathbb{N} denote the set of positive integers. The MZV is a real number that belongs to an image of a function (customarily denoted by ζ\zeta) whose domain is

𝐈\displaystyle{\bf I} =\displaystyle= r0{𝐤r=(k1,k2,,kr)r|k12},\displaystyle{\textstyle\bigcup\limits_{r\geq 0}}\{{\bf k}_{r}=(k_{1},k_{2},\ldots,k_{r})\in\mathbb{N}^{r}{\,|\,}k_{1}\geq 2\}, (1.1)

where 𝐤0={\bf k}_{0}=\varnothing is the empty mult-index and ζ()=1\zeta(\varnothing)=1. We call w(𝐤r)=k1++kr{\mathrm{w}}({\bf k}_{r})=k_{1}+\cdots+k_{r} and d(𝐤r)=r{\mathrm{d}}({\bf k}_{r})=r the weight and depth, respectively. The function ζ\zeta has two definitions by the iterated integral and nested summation, which endow the \mathbb{Q}-vector space 𝒵\mathcal{Z} spanned by MZVs with abundant linear relations. Euler [13], who solved the Basel problem ζ(2)=π2/6\zeta(2)=\pi^{2}/6 and advanced the case r=1r=1, also studied the case r=2r=2.

Zagier [34] conjectured111 Zagier noted the conjectures were made after many discussions with Drinfel’d, Kontsevich and Goncharov. that 𝒵\mathcal{Z} is graded by weight and the dimensions of graded pieces are expressed in terms of a Fibonacci-like sequence. Let 𝐈k{\bf I}_{k} be the subset consisting of mult-indices of weight kk, and let 𝒵k\mathcal{Z}_{k} be the subspace spanned by MZVs in ζ(𝐈k)={ζ(𝐤)|𝐤𝐈k}\zeta({\bf I}_{k})=\{\zeta({\bf k}){\,|\,}{\bf k}\in{\bf I}_{k}\}. The dimension conjecture is

dim𝒵k\displaystyle\dim_{\mathbb{Q}}\mathcal{Z}_{k} =?\displaystyle\overset{?}{=} dk,\displaystyle d_{k}, (1.2)

where dk=dk2+dk3d_{k}=d_{k-2}+d_{k-3} (k3)(k\geq 3), d0=d2=1d_{0}=d_{2}=1 and d1=0d_{1}=0. These integers fit together into the generating series

k0dkXk\displaystyle\sum_{k\geq 0}d_{k}X^{k} =\displaystyle= 11(X2+X3).\displaystyle\frac{1}{1-(X^{2}+X^{3})}. (1.3)

The ultimate upper bound theorem (i.e., dim𝒵kdk\dim_{\mathbb{Q}}\mathcal{Z}_{k}\leq d_{k}) was established independently by Goncharov [10, 16] and Terasoma [32]. Brown [8] furthermore proved that 𝒵k\mathcal{Z}_{k} is generated by MZVs in ζ(𝐈kH)\zeta({\bf I}^{H}_{k}), where 𝐈kH{\bf I}^{H}_{k} is the set of Hoffman mult-indices of weight kk:

𝐈kH\displaystyle{\bf I}^{H}_{k} =\displaystyle= {𝐤=(k1,,kr)𝐈k|ki{2,3}}.\displaystyle\{{\bf k}=(k_{1},\ldots,k_{r})\in{\bf I}_{k}{\,|\,}k_{i}\in\{2,3\}\}. (1.4)

Hoffman [18] conjectured ζ(𝐈kH)\zeta({\bf I}^{H}_{k}) is a basis of 𝒵k\mathcal{Z}_{k}, which would imply the dimension conjecture because the same recurrence relation |𝐈kH|=|𝐈k2H|+|𝐈k3H||{\bf I}^{H}_{k}|=|{\bf I}^{H}_{k-2}|+|{\bf I}^{H}_{k-3}| holds by a simple count of the number of 22’s and 33’s. Umezawa [33] also suggested a basis conjecture in terms of iterated log-sine integrals, in which sets of mult-indices different from 𝐈kH{\bf I}^{H}_{k} are used. Because of the difficulty to show the independence between MZVs, no non-trivial lower bounds are known.

By the upper bound theorem, it is natural to ask that what sorts of relations are needed to reduce the number of generators of 𝒵k\mathcal{Z}_{k} to dkd_{k}. There are several conjectural candidates: e.g., [11, 14, 17, 22, 23]. In particular, the extended double shuffle (EDS) relations [19, 29] known from early on are often selected for experimentally attacking this question, because they are easier to write down and included in the other candidates except Kawasima’s [23]. Minh and Petitot [28] verified that the class of EDS relations is a right candidate up to weight 1010, Bigotte et al.[5] verified it up to weight 1212, Minh et al.[27] verified it up to weight 1616,​222 This experimental result was announced in their private communication (see [21, Section 1]). Espie et al.[12] verified it up to weight 1919, and Kaneko et al.[21] verified it up to weight 2020 that seems to be the latest record. The first two experiments are by the Gröbner basis method, and the last three ones are by the vector space (or matrix) method. The fourth one of [12] was executed under modulo rational multiples of powers of ζ(2)\zeta(2), or module [ζ(2)]\mathbb{Q}[\zeta(2)].

The first purpose of this paper is to improve the record to weight k=22k=22. For this, we consider an 𝔽2\mathbb{F}_{2}-vector space 𝒵k𝔟\mathcal{Z}_{k}^{\mathfrak{b}} instead of the \mathbb{Q}-vector space 𝒵k\mathcal{Z}_{k}: roughly speaking, 𝒵k𝔟\mathcal{Z}_{k}^{\mathfrak{b}} is generated by binary multiple zeta symbols ζ𝔟(𝐤)\zeta^{\mathfrak{b}}({\bf k}) (𝐤𝐈k)({\bf k}\in{\bf I}_{k}) (binary MZSs for short), where ζ𝔟(𝐤)\zeta^{\mathfrak{b}}({\bf k}) satisfy binary EDS relations that are obtained from original EDS relations after the modulo 22 arithmetic to integer coefficients. (Exact definitions of the binary analogs in this section will be stated in the next section.) We will verify ζ𝔟(𝐈kH)\zeta^{\mathfrak{b}}({\bf I}^{H}_{k}) is a basis of 𝒵k𝔟\mathcal{Z}_{k}^{\mathfrak{b}} and dim𝔽2𝒵k𝔟=dk\dim_{\mathbb{F}_{2}}\mathcal{Z}_{k}^{\mathfrak{b}}=d_{k}. Our calculation results break the record because dim𝒵kdim𝔽2𝒵k𝔟\dim_{\mathbb{Q}}\mathcal{Z}_{k}\leq\dim_{\mathbb{F}_{2}}\mathcal{Z}_{k}^{\mathfrak{b}} (as will be mentioned in Section 3). The space 𝒵k𝔟\mathcal{Z}_{k}^{\mathfrak{b}} reduces the computation cost since 𝔽2\mathbb{F}_{2} is the binary and simplest finite field. The field 𝔽2\mathbb{F}_{2} makes it easy to apply useful techniques in computer since 𝔽2\mathbb{F}_{2} is compatible with the Boolean datatype: in fact, we will employ a conflict based algorithm discussed in [24] for a fast Gaussian forward elimination.

The second and main purpose is to observe a Pascal triangle pattern in 𝒵k𝔟\mathcal{Z}_{k}^{\mathfrak{b}} from the viewpoint of a direct sum decomposition,

𝒵k𝔟\displaystyle\mathcal{Z}_{k}^{\mathfrak{b}} \displaystyle\cong 𝒵¯k,k1𝔟𝒵¯k,0𝔟,\displaystyle\overline{\mathcal{Z}}_{k,k-1}^{\mathfrak{b}}\operatorname*{\bigoplus}\cdots\operatorname*{\bigoplus}\overline{\mathcal{Z}}_{k,0}^{\mathfrak{b}}, (1.5)

where 𝒵¯k,r𝔟\overline{\mathcal{Z}}_{k,r}^{\mathfrak{b}} are quotient spaces defined by means of depth filtration: the descending chain 𝒵k,k1𝔟𝒵k,0𝔟\mathcal{Z}_{k,k-1}^{\mathfrak{b}}\supset\cdots\supset\mathcal{Z}_{k,0}^{\mathfrak{b}} is used for 𝒵¯k,r𝔟=𝒵k,r𝔟/𝒵k,r1𝔟\overline{\mathcal{Z}}_{k,r}^{\mathfrak{b}}=\mathcal{Z}_{k,r}^{\mathfrak{b}}/\mathcal{Z}_{k,r-1}^{\mathfrak{b}}, where 𝒵k,r𝔟\mathcal{Z}_{k,r}^{\mathfrak{b}} are the subspaces spanned by binary MZSs of weight kk and depth at most rr. We define 𝐈k,r={𝐤𝐈k|d(𝐤)=r}{\bf I}_{k,r}=\{{\bf k}\in{\bf I}_{k}{\,|\,}{\mathrm{d}}({\bf k})=r\} and

𝐈k,rH\displaystyle{\bf I}^{H}_{k,r} =\displaystyle= 𝐈kH𝐈k,r,\displaystyle{\bf I}^{H}_{k}\cap{\bf I}_{k,r}, (1.6)

with dk,r𝔟=|𝐈k,rH|d^{\mathfrak{b}}_{k,r}=|{\bf I}^{H}_{k,r}|. We denote by ζ¯𝔟(𝐤)\overline{\zeta}^{\mathfrak{b}}({\bf k}) the canonical image333 We use the same notation ζ¯𝔟\overline{\zeta}^{\mathfrak{b}} for all canonical images in the quotient spaces 𝒵¯k,r𝔟\overline{\mathcal{Z}}_{k,r}^{\mathfrak{b}} (k>r0)(k>r\geq 0). There should be no confusion because the quotient space under consideration is clear from context. of ζ𝔟(𝐤)\zeta^{\mathfrak{b}}({\bf k}) in 𝒵¯k,r𝔟\overline{\mathcal{Z}}_{k,r}^{\mathfrak{b}} for any 𝐤𝐈k,r{\bf k}\in{\bf I}_{k,r}. Up to weight k=22k=22, we will verify ζ¯𝔟(𝐈k,rH)\overline{\zeta}^{\mathfrak{b}}({\bf I}^{H}_{k,r}) is a basis of 𝒵¯k,r𝔟\overline{\mathcal{Z}}_{k,r}^{\mathfrak{b}} and dim𝔽2𝒵¯k,r𝔟=dk,r𝔟\dim_{\mathbb{F}_{2}}\overline{\mathcal{Z}}_{k,r}^{\mathfrak{b}}=d^{\mathfrak{b}}_{k,r}. Counting the number of 22’s and 33’s implies that the double sequence (dk,r𝔟)(d^{\mathfrak{b}}_{k,r}) satisfies a recurrence relation with a Pascal triangle pattern: dk,r𝔟=dk2,r1𝔟+dk3,r1𝔟d^{\mathfrak{b}}_{k,r}=d^{\mathfrak{b}}_{k-2,r-1}+d^{\mathfrak{b}}_{k-3,r-1} (k3,r1)(k\geq 3,r\geq 1), d0,0𝔟=d2,1𝔟=1d^{\mathfrak{b}}_{0,0}=d^{\mathfrak{b}}_{2,1}=1 and dk,r𝔟=0d^{\mathfrak{b}}_{k,r}=0 for other kk and rr, or equivalently,

k,r0dk,r𝔟XkYr\displaystyle\sum_{k,r\geq 0}d^{\mathfrak{b}}_{k,r}X^{k}Y^{r} =\displaystyle= 11(X2+X3)Y.\displaystyle\frac{1}{1-(X^{2}+X^{3})Y}. (1.7)

More precisely, dk,r𝔟=(rk2r)d^{\mathfrak{b}}_{k,r}=\binom{r}{k-2r} since the integers Pr,k=dk+2r,r𝔟P_{r,k}=d^{\mathfrak{b}}_{k+2r,r} satisfy the same recurrence relation as the binomial coefficients (rk)\binom{r}{k}. As expected from (1.5), the formula (1.7) specializes to (1.3) upon Y=1Y=1.

We also try experiments on parts of EDS relations, ‘KNT\mathrm{KNT}’ and ‘MJPO\mathrm{MJPO}’ relations, which are expected to be alternatives to EDS and actually employed in [21, 27] for verification, respectively. Unlike the case in 𝒵k\mathcal{Z}_{k}, those relations do not suffice to give all relations in 𝒵k𝔟\mathcal{Z}_{k}^{\mathfrak{b}}, but we can find a quasi Fibonacci-like rule in dimensions of spaces defined by MJPO\mathrm{MJPO} relations.

The idea of the depth filtration in (1.5) was conceived by Broadhurst and Kreimer [7] to propose a refinement of the dimension conjecture. Their conjecture indicates two interesting facts in the \mathbb{Q}-vector spaces of MZVs graded by both weight and depth: (i) modular forms influence the structure through quotient spaces 𝒵¯k,r\overline{\mathcal{Z}}_{k,r} defined by the \mathbb{Q}-version of (1.5); and (ii) the Hoffman values ζ(𝐤)\zeta({\bf k}) (𝐤𝐈kH)({\bf k}\in{\bf I}^{H}_{k}) are irrelevant to the structure in the sense that most of the values vanish in the graded pieces of same depth. In terms of the generating series, the conjecture is

k,r0dim𝒵¯k,rXkYr\displaystyle\sum_{k,r\geq 0}\dim\overline{\mathcal{Z}}_{k,r}X^{k}Y^{r} =?\displaystyle\overset{?}{=} 1+E(X)Y1O(X)Y+S(X)Y2(1Y2),\displaystyle\frac{1+E(X)Y}{1-O(X)Y+S(X)Y^{2}(1-Y^{2})}, (1.8)

where E(X)=X2/(1X2)E(X)=X^{2}/(1-X^{2}), O(X)=X3/(1X2)O(X)=X^{3}/(1-X^{2}) and S(X)=X12/(1X4)(1X6)S(X)=X^{12}/(1-X^{4})(1-X^{6}), and S(X)S(X) is the generating series of the dimensions of the vector spaces of cusp forms on the full modular group. Specific examples for r=2r=2 are given in [15] and a modern formulation is discussed in [9] (see also [31]). However our computational results suggest the following when we adopt 𝔽2\mathbb{F}_{2} as the scalar field instead of \mathbb{Q}: (i) the influence of modular forms disappears; but (ii) the Hoffman symbols ζ𝔟(𝐤)\zeta^{\mathfrak{b}}({\bf k}) (𝐤𝐈kH)({\bf k}\in{\bf I}^{H}_{k}) remain as basis elements with a Pascal triangle pattern.

It should be noted that the Broadhurst-Kreimer conjecture has two equivalent formulations of vector and algebra (see [19, Appendix]). The equivalence requires [ζ(2)]\mathbb{Q}[\zeta(2)] is isomorphic to the polynomial ring in one variable over \mathbb{Q}. The isomorphy does not hold when 𝔽2\mathbb{F}_{2} is the scalar field as will be mentioned in the final section, and we will consider only the vector formulation in this paper.

It should also be noted that Blümlein et al.[6] provided a data mine for not only MZVs but also Euler sums by experiments to Broadhurst-Kreimer type conjectures, in which it was verified that the union of EDS and duality relations suffices to reduce the number of generators of 𝒵k\mathcal{Z}_{k} to dkd_{k} up to weight 2222: it was also verified up to 2424 by using modular arithmetic, and up to 2626 and more with an additional conjecture and limited depths. The duality relations, which are obtained by the integral definition of MZVs and a change of variables, are very useful to compute because they can bring down the size of relations by about half. It has not been proved yet that the EDS relations include the duality relations, although the inclusion is expected to be true conjecturally: in other words, we have not succeeded in understanding the duality of MZVs algebraically. The experimental approaches of [6] and ours differ in the use of the duality relations.

The organization of this paper is as follows. In Section 2, we state exact definitions of the binary MZVs ζ𝔟(𝐤)\zeta^{\mathfrak{b}}({\bf k}), the formal multiple zeta spaces 𝒵k𝔟\mathcal{Z}_{k}^{\mathfrak{b}} and the quotient spaces 𝒵¯k,r𝔟\overline{\mathcal{Z}}_{k,r}^{\mathfrak{b}}. We report our computational results in Section 3, and explain how our computer programs produce the results in Section 4. The programs are available at the open-source site GitHub.444https://github.com/machide-tomoyan/BMZS-calculator Section 5 is devoted to problems about formal multiple zeta spaces which arise from the computational results. In Appendix, we describe an essential algorithm in our experiments, which employs a conflict based search and speeds up the Gaussian forward elimination under certain conditions.

The computer only assists us in showing Proposition 4.1 by Gaussian elimination. The dimension conjecture (1.2) is true if we can theoretically show Proposition 4.1 for all weights kk.

2 Formal multiple zeta space over 𝔽2\mathbb{F}_{2}

The formal multiple zeta space 𝒵k𝔟\mathcal{Z}_{k}^{\mathfrak{b}} of weight kk is briefly defined by

𝒵k𝔟\displaystyle\mathcal{Z}_{k}^{\mathfrak{b}} =\displaystyle= η𝔟(𝐤)|𝐤𝐈k𝔽2 {binary EDS relations} ,\displaystyle\frac{\langle\eta^{\mathfrak{b}}({\bf k}){\,|\,}{\bf k}\in{\bf I}_{k}\rangle_{\mathbb{F}_{2}}}{\text{ \{binary EDS relations\} }}, (2.1)

where η𝔟(𝐤)\eta^{\mathfrak{b}}({\bf k}) are indeterminates. That is, 𝒵k𝔟\mathcal{Z}_{k}^{\mathfrak{b}} is an 𝔽2\mathbb{F}_{2}-vector space generated by formal symbols ζ𝔟(𝐤)η𝔟(𝐤)\zeta^{\mathfrak{b}}({\bf k})\equiv\eta^{\mathfrak{b}}({\bf k}) that satisfy binary variations of the EDS relations. Eight equivalent statements are given in [19, Theorem 2] for the EDS relations. In this paper, we choice the statement (v) in the theorem because the relations are all \mathbb{Z}-linear and fewer in number.

To define (2.1) exactly, we require the algebraic setup by Hoffman [18] which allows us the steady handling of two products, the shuffle sh\mathcyr{sh} and stuffle *: the latter is also called harmonic or quasi-shuffle. Let \mathfrak{H} be the polynomial ring x,y\mathbb{Q}\langle x,y\rangle in the two non-commutative variables xx and yy. We call each variable a letter, and a monomial in the variables a word. The shuffle product sh\mathcyr{sh} is a \mathbb{Q}-bilinear product on \mathfrak{H}, which satisfies w=wsh 1=1shww=w\;\mathcyr{sh}\;1=1\;\mathcyr{sh}\;w and

aushbv\displaystyle au\;\mathcyr{sh}\;bv =\displaystyle= a(ushbv)+b(aushv)\displaystyle a(u\;\mathcyr{sh}\;bv)+b(au\;\mathcyr{sh}\;v) (2.2)

for any words u,v,wu,v,w\in\mathfrak{H} and letters a,b{x,y}a,b\in\{x,y\}. Let zkz_{k} denote a word xk1yx^{k-1}y for any k1k\geq 1, and let 1\mathfrak{H}^{1} be the polynomial ring z1,z2,\mathbb{Q}\langle z_{1},z_{2},\ldots\rangle, or equivalently, the subring +y\mathbb{Q}+\mathfrak{H}y in \mathfrak{H}. The stuffle product * is a \mathbb{Q}-bilinear product on 1\mathfrak{H}^{1}, which satisfies w=w 1=1ww=w\,*\,1=1\,*\,w and

ziuzjv\displaystyle z_{i}u\,*\,z_{j}v =\displaystyle= zi(uzjv)+zj(ziuv)+zi+j(uv)\displaystyle z_{i}(u\,*\,z_{j}v)+z_{j}(z_{i}u\,*\,v)+z_{i+j}(u\,*\,v) (2.3)

for any words u,v,wu,v,w\in\mathfrak{H} and integers i,j1i,j\geq 1. By induction on the lengths of words, both products are commutative and associative, and both sh1=(1,sh)\mathfrak{H}^{1}_{\mathcyr{sh}}=(\mathfrak{H}^{1},\mathcyr{sh}) and 1=(1,)\mathfrak{H}^{1}_{*}=(\mathfrak{H}^{1},*) are commutative \mathbb{Q}-algebras. We notice sh=(,sh)\mathfrak{H}_{\mathcyr{sh}}=(\mathfrak{H},\mathcyr{sh}) is a parent space of sh1\mathfrak{H}^{1}_{\mathcyr{sh}}. Let 0=+xy=z𝐤|𝐤𝐈\mathfrak{H}^{0}=\mathbb{Q}+x\mathfrak{H}y=\langle z_{{\bf k}}{\,|\,}{\bf k}\in{\bf I}\rangle_{\mathbb{Q}}, where z𝐤=zk1zkrz_{{\bf k}}=z_{k_{1}}\cdots z_{k_{r}} and z=1z_{\varnothing}=1. Both sh0=(0,sh)\mathfrak{H}^{0}_{\mathcyr{sh}}=(\mathfrak{H}^{0},\mathcyr{sh}) and 0=(0,)\mathfrak{H}^{0}_{*}=(\mathfrak{H}^{0},*) are subalgebras since 0\mathfrak{H}^{0} is closed under sh\mathcyr{sh} and *. The pair (1,0)(\mathfrak{H}^{1},\mathfrak{H}^{0}) of spaces satisfies the polynomial ring property in one variable: the former is freely generated by yy over the latter on each of sh\mathcyr{sh} and *. We thus have

sh1sh0[y],10[y].\displaystyle\mathfrak{H}^{1}_{\mathcyr{sh}}\,\simeq\,\mathfrak{H}^{0}_{\mathcyr{sh}}[y],\qquad\mathfrak{H}^{1}_{*}\,\simeq\,\mathfrak{H}^{0}_{*}[y]. (2.4)

See [30] and [18] for proofs of (2.4), respectively.

We introduce the EDS relations stated in [19, Theorem 2(v)]. Let regsh\mathrm{reg}_{\mathcyr{sh}} denote a homomorphism from sh1\mathfrak{H}^{1}_{\mathcyr{sh}} to sh0\mathfrak{H}^{0}_{\mathcyr{sh}}, which is defined by taking the constant term with respect to yy in the first isomorphism of (2.4):555 The homomorphism reg\mathrm{reg}_{*} of stuffle type exists as well, but it is intractable because EDS relations of that type are not always \mathbb{Z}-linear: see [19] (or [2, 20]) for details.

regsh:sh1w=i=0mwishyshiw00.\displaystyle\mathrm{reg}_{\mathcyr{sh}}:\mathfrak{H}^{1}_{\mathcyr{sh}}\,\ni\,w\,=\,\sum_{i=0}^{m}w_{i}\;\mathcyr{sh}\;y^{\mathcyr{sh}i}\quad\mapsto\quad w_{0}\,\in\,\mathfrak{H}^{0}. (2.5)

Let 𝐈^k=𝐈k{(1,,1𝑘)}{\bf\widehat{I}}_{k}={\bf I}_{k}\cup\{(\underset{k}{\underbrace{1,\ldots,1}})\}, and let

𝐏𝐈^k\displaystyle\widehat{{\bf PI}}_{k} =\displaystyle= i,j0(i+j=k)𝐈^i×𝐈j.\displaystyle{\textstyle\bigcup\limits_{i,j\geq 0\atop(i+j=k)}}{\bf\widehat{I}}_{i}\times{\bf I}_{j}.

For any pair (𝐤,𝐥)({\bf k},{\bf l}) of mult-indices in 𝐏𝐈^k\widehat{{\bf PI}}_{k}, we define

𝖽𝗌(𝐤,𝐥)\displaystyle\mathsf{ds}({\bf k},{\bf l}) :=\displaystyle:= regsh(z𝐤z𝐥)regsh(z𝐤shz𝐥)0.\displaystyle\mathrm{reg}_{\mathcyr{sh}}(z_{{\bf k}}\,*\,z_{{\bf l}})-\mathrm{reg}_{\mathcyr{sh}}(z_{{\bf k}}\;\mathcyr{sh}\;z_{{\bf l}})\,\in\,\mathfrak{H}^{0}. (2.6)

The objective EDS relations of weight kk are stated as

Z(𝖽𝗌(𝐤,𝐥))\displaystyle Z(\mathsf{ds}({\bf k},{\bf l})) =\displaystyle= 0((𝐤,𝐥)𝐏𝐈^k),\displaystyle 0\qquad(({\bf k},{\bf l})\in\widehat{{\bf PI}}_{k}), (2.7)

where Z:0Z:\mathfrak{H}^{0}\to\mathbb{R} is the \mathbb{Q}-linear map (or evaluation map) defined by Z(z𝐤)=ζ(𝐤)Z(z_{{\bf k}})=\zeta({\bf k}) (𝐤𝐈)({\bf k}\in{\bf I}). We have by (2.5)

regsh(w)\displaystyle\mathrm{reg}_{\mathcyr{sh}}(w) =\displaystyle= w(w0),\displaystyle w\qquad(w\in\mathfrak{H}^{0}),
regsh(ymshz𝐦)\displaystyle\rule{0.0pt}{15.0pt}\mathrm{reg}_{\mathcyr{sh}}(y^{m}\;\mathcyr{sh}\;z_{{\bf m}}) =\displaystyle= 0(m>0,𝐦𝐈).\displaystyle 0\qquad(m>0,{\bf m}\in{\bf I}).

We can thus divide (2.7) into two parts:

Z(z𝐤z𝐥)Z(z𝐤shz𝐥)\displaystyle Z(z_{{\bf k}}\,*\,z_{{\bf l}})-Z(z_{{\bf k}}\;\mathcyr{sh}\;z_{{\bf l}}) =\displaystyle= 0((𝐤,𝐥)𝐏𝐈k),\displaystyle 0\qquad(({\bf k},{\bf l})\in{\bf PI}_{k}), (2.8)
Z(regsh(ymz𝐦))\displaystyle\rule{0.0pt}{15.0pt}Z(\mathrm{reg}_{\mathcyr{sh}}(y^{m}\,*\,z_{{\bf m}})) =\displaystyle= 0(0<m<k1,𝐦𝐈km),\displaystyle 0\qquad(0<m<k-1,{\bf m}\in{\bf I}_{k-m}), (2.9)

where 𝐏𝐈k=i,j0(i+j=k)𝐈i×𝐈j{\bf PI}_{k}={\textstyle\bigcup_{i,j\geq 0\atop(i+j=k)}}{\bf I}_{i}\times{\bf I}_{j}. The relations in (2.8) are called the finite double shuffle (FDS) relations, because MZVs are defined by ζ(k1,,kr)=m1>>mr>01/m1k1mrkr\zeta(k_{1},\ldots,k_{r})=\sum_{m_{1}>\cdots>m_{r}>0}1/m_{1}^{k_{1}}\cdots m_{r}^{k_{r}} and finite (or convergent) at 𝐤𝐈{\bf k}\in{\bf I}. The FDS relations do not suffice to give all relations of MZVs. For instance, we can not obtain any relation in weight 33, in particular, the simplest formula ζ(2,1)=ζ(3)\zeta(2,1)=\zeta(3). Therefore the relations in (2.9) are essential to the EDS conjecture.

A little more notions are required for (2.1), which are analogs of the notions mentioned above in \mathbb{Z}-module and 𝔽2\mathbb{F}_{2}-vector. Let \mathfrak{H}^{\mathbb{Z}} denote the subring x,y\mathbb{Z}\langle x,y\rangle in x,y\mathbb{Q}\langle x,y\rangle. We set

,0=z𝐤|𝐤𝐈,𝔟=η𝔟(𝐤)|𝐤𝐈𝔽2,\displaystyle\mathfrak{H}^{\mathbb{Z},0}\,=\,\langle z_{{\bf k}}{\,|\,}{\bf k}\in{\bf I}\rangle_{\mathbb{Z}},\qquad\mathcal{H}^{\mathfrak{b}}\,=\,\langle\eta^{\mathfrak{b}}({\bf k}){\,|\,}{\bf k}\in{\bf I}\rangle_{\mathbb{F}_{2}},

to define a canonical map from ,0\mathfrak{H}^{\mathbb{Z},0} to 𝔟\mathcal{H}^{\mathfrak{b}} which is induced by modulo 22 arithmetic:

can𝔟:,0w=𝐤𝐈c𝐤z𝐤𝐤𝐈(c𝐤mod 2)η𝔟(𝐤)𝔟.\displaystyle\mathrm{can}^{\mathfrak{b}}:\mathfrak{H}^{\mathbb{Z},0}\,\ni\,w\,=\,\sum_{{\bf k}\in{\bf I}}c_{{\bf k}}z_{{\bf k}}\quad\mapsto\quad\sum_{{\bf k}\in{\bf I}}(c_{{\bf k}}\,\mathrm{mod}\ 2)\eta^{\mathfrak{b}}({\bf k})\,\in\,\mathcal{H}^{\mathfrak{b}}. (2.10)

For any pair (𝐤,𝐥)𝐏𝐈k({\bf k},{\bf l})\in{\bf PI}_{k}, the elements z𝐤z𝐥z_{{\bf k}}\,*\,z_{{\bf l}} and z𝐤shz𝐥z_{{\bf k}}\;\mathcyr{sh}\;z_{{\bf l}} belong to ,0\mathfrak{H}^{\mathbb{Z},0}, and the element can𝔟(𝖽𝗌(𝐤,𝐥))\mathrm{can}^{\mathfrak{b}}(\mathsf{ds}({\bf k},{\bf l})) is well-defined. For 0<m<k10<m<k-1 and 𝐦𝐈km{\bf m}\in{\bf I}_{k-m}, ymz𝐦y^{m}\,*\,z_{{\bf m}} belongs to ynz𝐧|n0,𝐧𝐈\langle y^{n}z_{{\bf n}}{\,|\,}n\geq 0,{\bf n}\in{\bf I}\rangle_{\mathbb{Z}}, and can𝔟(regsh(ymz𝐦))\mathrm{can}^{\mathfrak{b}}(\mathrm{reg}_{\mathcyr{sh}}(y^{m}\,*\,z_{{\bf m}})) is well-defined if

regsh(ynz𝐧)\displaystyle\mathrm{reg}_{\mathcyr{sh}}(y^{n}z_{{\bf n}}) \displaystyle\in ,0(n>0,𝐧𝐈),\displaystyle\mathfrak{H}^{\mathbb{Z},0}\qquad(n>0,{\bf n}\in{\bf I}),

which holds by [19, Proposition 8] (see (4.7) below). Consequently,

k𝔟\displaystyle\mathcal{E}^{\mathfrak{b}}_{k} :=\displaystyle:= can𝔟(𝖽𝗌(𝐤,𝐥))|(𝐤,𝐥)𝐏𝐈^k𝔽2k𝔟\displaystyle\langle\mathrm{can}^{\mathfrak{b}}(\mathsf{ds}({\bf k},{\bf l})){\,|\,}({\bf k},{\bf l})\in\widehat{{\bf PI}}_{k}\rangle_{\mathbb{F}_{2}}\,\subset\,\mathcal{H}^{\mathfrak{b}}_{k}

is well-defined.

We are in a position to define (2.1).

Definition 2.1.

For a weight kk, we define the formal multiple zeta space by

𝒵k𝔟\displaystyle\mathcal{Z}_{k}^{\mathfrak{b}} :=\displaystyle:= k𝔟/k𝔟.\displaystyle\mathcal{H}^{\mathfrak{b}}_{k}/\mathcal{E}^{\mathfrak{b}}_{k}. (2.11)

For a mult-index 𝐤𝐈k{\bf k}\in{\bf I}_{k}, we denote by ζ𝔟(𝐤)\zeta^{\mathfrak{b}}({\bf k}) the element in 𝒵k𝔟\mathcal{Z}_{k}^{\mathfrak{b}} which is congruent to η𝔟(𝐤)\eta^{\mathfrak{b}}({\bf k}) modulo k𝔟\mathcal{E}^{\mathfrak{b}}_{k}. We call ζ𝔟(𝐤)\zeta^{\mathfrak{b}}({\bf k}) a binary multiple zeta symbol or a binary MZS.

Let H𝔟H^{\mathfrak{b}} denote the natural homomorphism from k0k𝔟\operatorname*{\bigoplus}_{k\geq 0}\mathcal{H}^{\mathfrak{b}}_{k} to k0𝒵k𝔟\operatorname*{\bigoplus}_{k\geq 0}\mathcal{Z}_{k}^{\mathfrak{b}}: each component is the canonical map of (2.11). We define the binary evaluation map by Z𝔟=H𝔟can𝔟Z^{\mathfrak{b}}=H^{\mathfrak{b}}\circ\mathrm{can}^{\mathfrak{b}}. The binary EDS relations of weight kk are then stated as

Z𝔟(𝖽𝗌(𝐤,𝐥))\displaystyle Z^{\mathfrak{b}}(\mathsf{ds}({\bf k},{\bf l})) =\displaystyle= 0((𝐤,𝐥)𝐏𝐈^k).\displaystyle 0\qquad(({\bf k},{\bf l})\in\widehat{{\bf PI}}_{k}). (2.12)

We list some examples of the original and binary EDS relations for weights k4k\leq 4 in Table 1.

Let 𝒵k,r𝔟\mathcal{Z}_{k,r}^{\mathfrak{b}} denote the vector subspace ζ𝔟(𝐤)|𝐤𝐈k,d(𝐤)r𝔽2\langle\zeta^{\mathfrak{b}}({\bf k}){\,|\,}{\bf k}\in{\bf I}_{k},{\mathrm{d}}({\bf k})\leq r\rangle_{\mathbb{F}_{2}} as introduced in the first section. We end this section with the definition of the graded pieces satisfying the direct sum decomposition (1.5).

Definition 2.2.

For a weight kk, we define the depth graded formal multiple zeta spaces by

𝒵¯k,r𝔟\displaystyle\overline{\mathcal{Z}}_{k,r}^{\mathfrak{b}} :=\displaystyle:= 𝒵k,r𝔟/𝒵k,r1𝔟(k>r0),\displaystyle\mathcal{Z}_{k,r}^{\mathfrak{b}}/\mathcal{Z}_{k,r-1}^{\mathfrak{b}}\qquad(k>r\geq 0), (2.13)

where 𝒵k,1𝔟={0}\mathcal{Z}_{k,-1}^{\mathfrak{b}}=\{0\}.

Table 1: EDS relations in 𝒵k\mathcal{Z}_{k} and 𝒵k𝔟\mathcal{Z}_{k}^{\mathfrak{b}} for weights k4k\leq 4.

𝐤,𝐥{\bf k},{\bf l} Original EDS relation (over \mathbb{Z}) Binary EDS relation (over 𝔽2\mathbb{F}_{2})
(1),(2)(1),(2) ζ(2,1)+ζ(3)=0-\zeta(2,1)+\zeta(3)=0 ζ𝔟(2,1)+ζ𝔟(3)=0\zeta^{\mathfrak{b}}(2,1)+\zeta^{\mathfrak{b}}(3)=0
(1),(3)(1),(3) ζ(2,2)ζ(3,1)+ζ(4)=0-\zeta(2,2)-\zeta(3,1)+\zeta(4)=0 ζ𝔟(2,2)+ζ𝔟(3,1)+ζ𝔟(4)=0\zeta^{\mathfrak{b}}(2,2)+\zeta^{\mathfrak{b}}(3,1)+\zeta^{\mathfrak{b}}(4)=0
(1),(2,1)(1),(2,1) ζ(2,1,1)+ζ(2,2)+ζ(3,1)=0-\zeta(2,1,1)+\zeta(2,2)+\zeta(3,1)=0 ζ𝔟(2,1,1)+ζ𝔟(2,2)+ζ𝔟(3,1)=0\zeta^{\mathfrak{b}}(2,1,1)+\zeta^{\mathfrak{b}}(2,2)+\zeta^{\mathfrak{b}}(3,1)=0
(1,1),(2)(1,1),(2) ζ(2,1,1)ζ(2,2)ζ(3,1)=0\zeta(2,1,1)-\zeta(2,2)-\zeta(3,1)=0 ζ𝔟(2,1,1)+ζ𝔟(2,2)+ζ𝔟(3,1)=0\zeta^{\mathfrak{b}}(2,1,1)+\zeta^{\mathfrak{b}}(2,2)+\zeta^{\mathfrak{b}}(3,1)=0
(2),(2)(2),(2) 4ζ(3,1)+ζ(4)=0-4\zeta(3,1)+\zeta(4)=0 ζ𝔟(4)=0\zeta^{\mathfrak{b}}(4)=0

3 Computational result

We report our computational results. How we obtain them will be explained in the next section.

We begin with a typical result related to (1.2).

Experiment 3.1.

For any weight kk with 2k222\leq k\leq 22, we verify ζ𝔟(𝐈kH)\zeta^{\mathfrak{b}}({\bf I}^{H}_{k}) is a basis of 𝒵k𝔟\mathcal{Z}_{k}^{\mathfrak{b}}, and

dim𝔽2𝒵k𝔟\displaystyle\dim_{\mathbb{F}_{2}}\mathcal{Z}_{k}^{\mathfrak{b}} =\displaystyle= 2k2dim𝔽2k𝔟=dk.\displaystyle 2^{k-2}-\dim_{\mathbb{F}_{2}}\mathcal{E}^{\mathfrak{b}}_{k}\,=\,d_{k}. (3.1)

The EDS conjecture states that, for every weight kk, the relations in (2.7) suffice to reduce the number of generators of 𝒵k\mathcal{Z}_{k} to dkd_{k}:

dimk\displaystyle\dim_{\mathbb{Q}}\mathcal{E}_{k} \displaystyle\geq 2k2dk,\displaystyle 2^{k-2}-d_{k}, (3.2)

where k=𝖽𝗌(𝐤,𝐥)|(𝐤,𝐥)𝐏𝐈^k\mathcal{E}_{k}=\langle\mathsf{ds}({\bf k},{\bf l}){\,|\,}({\bf k},{\bf l})\in\widehat{{\bf PI}}_{k}\rangle_{\mathbb{Q}}. This can be confirmed by Experiment 3.1, as follows. We denote by k=𝖽𝗌(𝐤,𝐥)|(𝐤,𝐥)𝐏𝐈^k\mathcal{E}^{\mathbb{Z}}_{k}=\langle\mathsf{ds}({\bf k},{\bf l}){\,|\,}({\bf k},{\bf l})\in\widehat{{\bf PI}}_{k}\rangle_{\mathbb{Z}} the \mathbb{Z}-module counterpart of k\mathcal{E}_{k}. Since \mathbb{Q} is the field of fractions of \mathbb{Z} and can𝔟\mathrm{can}^{\mathfrak{b}} is a surjective homomorphism from k\mathcal{E}^{\mathbb{Z}}_{k} to k𝔟\mathcal{E}^{\mathfrak{b}}_{k},

dimk\displaystyle\dim_{\mathbb{Q}}\mathcal{E}_{k} =\displaystyle= rankkdim𝔽2k𝔟,\displaystyle\mathrm{rank}_{\mathbb{Z}}\,\mathcal{E}^{\mathbb{Z}}_{k}\,\geq\,\dim_{\mathbb{F}_{2}}\mathcal{E}^{\mathfrak{b}}_{k},

which, together with (3.1), proves (3.2) for k22k\leq 22.

We recall dk,r𝔟=(rk2r)d^{\mathfrak{b}}_{k,r}=\binom{r}{k-2r} that is the number of the Hoffman mult-indices of weight kk and depth rr. We define k,r𝔟=η𝔟(𝐤)|𝐤𝐈k,d(𝐤)r𝔽2k𝔟\mathcal{H}^{\mathfrak{b}}_{k,r}=\langle\eta^{\mathfrak{b}}({\bf k}){\,|\,}{\bf k}\in{\bf I}_{k},{\mathrm{d}}({\bf k})\leq r\rangle_{\mathbb{F}_{2}}\subset\mathcal{H}^{\mathfrak{b}}_{k}, and

¯k,r𝔟\displaystyle\overline{\mathcal{E}}^{\mathfrak{b}}_{k,r} =\displaystyle= (k,r𝔟k𝔟)/k,r1𝔟.\displaystyle(\mathcal{H}^{\mathfrak{b}}_{k,r}\cap\mathcal{E}^{\mathfrak{b}}_{k})/\mathcal{H}^{\mathfrak{b}}_{k,r-1}.

The main result is a refinement of Experiment 3.1. Taking the sum for r=1,,k1r=1,\ldots,k-1 in (3.3) induces (3.1) because of (1.5): note that 𝒵¯k,0𝔟={0}\overline{\mathcal{Z}}_{k,0}^{\mathfrak{b}}=\{0\} unless k=0k=0.

Experiment 3.2.

For any weight kk and depth rr with 1r<k221\leq r<k\leq 22, we verify ζ¯𝔟(𝐈k,rH)\overline{\zeta}^{\mathfrak{b}}({\bf I}^{H}_{k,r}) is a basis of 𝒵¯k,r𝔟\overline{\mathcal{Z}}_{k,r}^{\mathfrak{b}}, and

dim𝔽2𝒵¯k,r𝔟\displaystyle\dim_{\mathbb{F}_{2}}\overline{\mathcal{Z}}_{k,r}^{\mathfrak{b}} =\displaystyle= (k2r1)dim𝔽2¯k,r𝔟=dk,r𝔟.\displaystyle\binom{k-2}{r-1}-\dim_{\mathbb{F}_{2}}\overline{\mathcal{E}}^{\mathfrak{b}}_{k,r}\,=\,d^{\mathfrak{b}}_{k,r}. (3.3)

The first equality in (3.3) is by the isomorphism theorems. In fact, we have

𝒵k,r𝔟/𝒵k,r1𝔟\displaystyle\mathcal{Z}_{k,r}^{\mathfrak{b}}/\mathcal{Z}_{k,r-1}^{\mathfrak{b}} \displaystyle\simeq k,r𝔟/(k,r𝔟k𝔟)/k,r1𝔟/(k,r1𝔟k𝔟)\displaystyle\raisebox{4.0pt}[0.0pt][0.0pt]{$\mathcal{H}^{\mathfrak{b}}_{k,r}/(\mathcal{H}^{\mathfrak{b}}_{k,r}\cap\mathcal{E}^{\mathfrak{b}}_{k})$}\Big{/}\raisebox{-4.0pt}[0.0pt][0.0pt]{$\mathcal{H}^{\mathfrak{b}}_{k,r-1}/(\mathcal{H}^{\mathfrak{b}}_{k,r-1}\cap\mathcal{E}^{\mathfrak{b}}_{k})$} (3.4)
\displaystyle\simeq k,r𝔟/(k,r1𝔟+k,r𝔟k𝔟)\displaystyle\rule{0.0pt}{15.0pt}\raisebox{4.0pt}[0.0pt][0.0pt]{$\mathcal{H}^{\mathfrak{b}}_{k,r}$}\Big{/}\raisebox{-4.0pt}[0.0pt][0.0pt]{$(\mathcal{H}^{\mathfrak{b}}_{k,r-1}+\mathcal{H}^{\mathfrak{b}}_{k,r}\cap\mathcal{E}^{\mathfrak{b}}_{k})$}
\displaystyle\simeq k,r𝔟/k,r1𝔟/(k,r𝔟k𝔟)/k,r1𝔟,\displaystyle\rule{0.0pt}{15.0pt}\raisebox{4.0pt}[0.0pt][0.0pt]{$\mathcal{H}^{\mathfrak{b}}_{k,r}/\mathcal{H}^{\mathfrak{b}}_{k,r-1}$}\Big{/}\raisebox{-4.0pt}[0.0pt][0.0pt]{$(\mathcal{H}^{\mathfrak{b}}_{k,r}\cap\mathcal{E}^{\mathfrak{b}}_{k})/\mathcal{H}^{\mathfrak{b}}_{k,r-1}$},

and

𝒵¯k,r𝔟\displaystyle\overline{\mathcal{Z}}_{k,r}^{\mathfrak{b}} \displaystyle\simeq k,r𝔟/k,r1𝔟/¯k,r𝔟.\displaystyle\raisebox{4.0pt}[0.0pt][0.0pt]{$\mathcal{H}^{\mathfrak{b}}_{k,r}/\mathcal{H}^{\mathfrak{b}}_{k,r-1}$}\Big{/}\raisebox{-4.0pt}[0.0pt][0.0pt]{$\overline{\mathcal{E}}^{\mathfrak{b}}_{k,r}$}.

Since (k2r1)=|k,r𝔟/k,r1𝔟|\binom{k-2}{r-1}=|\mathcal{H}^{\mathfrak{b}}_{k,r}/\mathcal{H}^{\mathfrak{b}}_{k,r-1}| by counting the number of the mult-indices of weight kk and depth rr, we obtain the desired equality.

We demonstrate the numbers dk,r𝔟d^{\mathfrak{b}}_{k,r} for k22k\leq 22 in Table 2. They are expressed in terms of binomial coefficients, and we can observe a (shifted) Pascal triangle pattern: the column r=0r=0 has the sequence (1)(1) from the row k=0k=0, the column r=1r=1 has (1,1)(1,1) from k=2k=2, the column r=2r=2 has (1,2,1)(1,2,1) from k=4k=4, the column r=3r=3 has (1,3,3,1)(1,3,3,1) from k=6k=6, and so on. For comparison, the dimensions of 𝒵¯k,r{\overline{\mathcal{Z}}_{k,r}} conjectured in (1.8) are listed in Table 3.

Table 2: The numbers dk,r𝔟d^{\mathfrak{b}}_{k,r} for 0r<k220\leq r<k\leq 22: the unlisted numbers dk,r𝔟d^{\mathfrak{b}}_{k,r} (r>11)(r>11) are 0. The total number of each row is dkd_{k} and that of each column is 2r2^{r} (for r7r\leq 7).

k/r01234567891011Total010000000000011000000000000020100000000001301000000000014001000000000150020000000002600110000000027000300000000380003100000004900014000000051000006100000071100004500000091200001101000001213000001060000016140000051510000211500000120700002816000000152110003717000000635800049180000001352810065190000000215690086200000000770361011421000000015684100151220000000028126451200Total1248163264128{\left.\begin{array}[]{|c|cccccccccccc|c|}\hline\cr k/r&0&1&2&3&4&5&6&7&8&9&10&11&\text{Total}\\ \hline\cr 0&1&0&0&0&0&0&0&0&0&0&0&0&1\\ 1&0&0&0&0&0&0&0&0&0&0&0&0&0\\ 2&0&1&0&0&0&0&0&0&0&0&0&0&1\\ 3&0&1&0&0&0&0&0&0&0&0&0&0&1\\ 4&0&0&1&0&0&0&0&0&0&0&0&0&1\\ 5&0&0&2&0&0&0&0&0&0&0&0&0&2\\ 6&0&0&1&1&0&0&0&0&0&0&0&0&2\\ 7&0&0&0&3&0&0&0&0&0&0&0&0&3\\ 8&0&0&0&3&1&0&0&0&0&0&0&0&4\\ 9&0&0&0&1&4&0&0&0&0&0&0&0&5\\ 10&0&0&0&0&6&1&0&0&0&0&0&0&7\\ 11&0&0&0&0&4&5&0&0&0&0&0&0&9\\ 12&0&0&0&0&1&10&1&0&0&0&0&0&12\\ 13&0&0&0&0&0&10&6&0&0&0&0&0&16\\ 14&0&0&0&0&0&5&15&1&0&0&0&0&21\\ 15&0&0&0&0&0&1&20&7&0&0&0&0&28\\ 16&0&0&0&0&0&0&15&21&1&0&0&0&37\\ 17&0&0&0&0&0&0&6&35&8&0&0&0&49\\ 18&0&0&0&0&0&0&1&35&28&1&0&0&65\\ 19&0&0&0&0&0&0&0&21&56&9&0&0&86\\ 20&0&0&0&0&0&0&0&7&70&36&1&0&114\\ 21&0&0&0&0&0&0&0&1&56&84&10&0&151\\ 22&0&0&0&0&0&0&0&0&28&126&45&1&200\\ \hline\cr\text{Total}&1&2&4&8&16&32&64&128&-&-&-&-&\lx@intercol\hfil\hfil\lx@intercol\\ \cline{1-13}\cr\end{array}\right.}

Table 3: The conjectural numbers dim𝒵¯k,r\dim\overline{\mathcal{Z}}_{k,r} for 0r<k220\leq r<k\leq 22: the unlisted numbers dim𝒵¯k,r\dim\overline{\mathcal{Z}}_{k,r} (r>11)(r>11) are 0.

k/r01234567891011Total01000000000001100000000000002010000000000130100000000001401000000000015011000000000260110000000002701200000000038012100000000490131000000005100133000000007110143100000009120136200000001213015640000000161401594200000021150168103000000281601514116000000371701713187300000491801619181740000065190181731191000000862001725303512400001142101922483729500001512201832456533160000200{\left.\begin{array}[]{|c|cccccccccccc|c|}\hline\cr k/r&0&1&2&3&4&5&6&7&8&9&10&11&\text{Total}\\ \hline\cr 0&1&0&0&0&0&0&0&0&0&0&0&0&1\\ 1&0&0&0&0&0&0&0&0&0&0&0&0&0\\ 2&0&1&0&0&0&0&0&0&0&0&0&0&1\\ 3&0&1&0&0&0&0&0&0&0&0&0&0&1\\ 4&0&1&0&0&0&0&0&0&0&0&0&0&1\\ 5&0&1&1&0&0&0&0&0&0&0&0&0&2\\ 6&0&1&1&0&0&0&0&0&0&0&0&0&2\\ 7&0&1&2&0&0&0&0&0&0&0&0&0&3\\ 8&0&1&2&1&0&0&0&0&0&0&0&0&4\\ 9&0&1&3&1&0&0&0&0&0&0&0&0&5\\ 10&0&1&3&3&0&0&0&0&0&0&0&0&7\\ 11&0&1&4&3&1&0&0&0&0&0&0&0&9\\ 12&0&1&3&6&2&0&0&0&0&0&0&0&12\\ 13&0&1&5&6&4&0&0&0&0&0&0&0&16\\ 14&0&1&5&9&4&2&0&0&0&0&0&0&21\\ 15&0&1&6&8&10&3&0&0&0&0&0&0&28\\ 16&0&1&5&14&11&6&0&0&0&0&0&0&37\\ 17&0&1&7&13&18&7&3&0&0&0&0&0&49\\ 18&0&1&6&19&18&17&4&0&0&0&0&0&65\\ 19&0&1&8&17&31&19&10&0&0&0&0&0&86\\ 20&0&1&7&25&30&35&12&4&0&0&0&0&114\\ 21&0&1&9&22&48&37&29&5&0&0&0&0&151\\ 22&0&1&8&32&45&65&33&16&0&0&0&0&200\\ \hline\cr\end{array}\right.}

Refinements of the EDS conjecture have been proposed. Minh et al.[27] conjectured that a part of the EDS relations obtained from

𝐏𝐈^kMJPO\displaystyle\widehat{{\bf PI}}_{k}^{\mathrm{MJPO}} =\displaystyle= 𝐏𝐈k(𝐈^1×𝐈k1)\displaystyle{\bf PI}_{k}\cup({\bf\widehat{I}}_{1}\times{\bf I}_{k-1}) (3.5)

is a right candidate, and verified it up to k=16k=16. The relations

Z(𝖽𝗌(𝐤,𝐥))\displaystyle Z(\mathsf{ds}({\bf k},{\bf l})) =\displaystyle= 0((𝐤,𝐥)𝐈^1×𝐈k1)\displaystyle 0\qquad(({\bf k},{\bf l})\in{\bf\widehat{I}}_{1}\times{\bf I}_{k-1})

are known as Hoffman’s relations ([18]), and their conjecture says that FDS relations and Hoffman’s relations suffice to give all relations among MZVs. Kaneko et al.[21] conjectured the above relations are too much, i.e., a smaller part obtained from

𝐏𝐈^kKNT\displaystyle\widehat{{\bf PI}}_{k}^{\mathrm{KNT}} =\displaystyle= ({(3),(2,1)}×𝐈k3)({(2)}×𝐈k2)(𝐈^1×𝐈k1)\displaystyle(\{(3),(2,1)\}\times{\bf I}_{k-3})\cup(\{(2)\}\times{\bf I}_{k-2})\cup({\bf\widehat{I}}_{1}\times{\bf I}_{k-1}) (3.6)

is a right candidate. They verified it up to k=20k=20.

In the space 𝒵k𝔟\mathcal{Z}_{k}^{\mathfrak{b}}, neither the relations obtained from (3.5) nor those obtained from (3.6) suffice to give all relations among binary MZSs.

Experiment 3.3.

Let {KNT,MJPO}\bullet\in\{\mathrm{KNT},\mathrm{MJPO}\} and let k𝔟,=can𝔟(𝖽𝗌(𝐤,𝐥))|(𝐤,𝐥)𝐏𝐈^k𝔽2\mathcal{E}^{\mathfrak{b},\bullet}_{k}=\langle\mathrm{can}^{\mathfrak{b}}(\mathsf{ds}({\bf k},{\bf l})){\,|\,}({\bf k},{\bf l})\in\widehat{{\bf PI}}_{k}^{\bullet}\rangle_{\mathbb{F}_{2}}. There exist weights k22k\leq 22 such that

dim𝔽2k𝔟,\displaystyle\dim_{\mathbb{F}_{2}}\mathcal{E}^{\mathfrak{b},\bullet}_{k} <\displaystyle< 2k2dk.\displaystyle 2^{k-2}-d_{k}. (3.7)
Table 4: The numbers dk𝔟,d^{\mathfrak{b},\bullet}_{k} ({KNT,MJPO})(\bullet\in\{\mathrm{KNT},\mathrm{MJPO}\}) with dkd_{k}: they are same when k6k\leq 6.

kdk𝔟,KNTdk𝔟,MJPOdk7443864498651012871121109123014121344181614662421151003328161404237172085749183007565194419986206441321142117415122231200\left.\begin{array}[]{|c|ccc|}\hline\cr k\rule[-6.0pt]{0.0pt}{20.0pt}&d^{\mathfrak{b},\mathrm{KNT}}_{k}&d^{\mathfrak{b},\mathrm{MJPO}}_{k}&d_{k}\\ \hline\cr 7&4&4&3\\ 8&6&4&4\\ 9&8&6&5\\ 10&12&8&7\\ 11&21&10&9\\ 12&30&14&12\\ 13&44&18&16\\ 14&66&24&21\\ 15&100&33&28\\ 16&140&42&37\\ 17&208&57&49\\ 18&300&75&65\\ 19&441&99&86\\ 20&644&132&114\\ 21&-&174&151\\ 22&-&231&200\\ \hline\cr\end{array}\right.

Computational results of dk𝔟,=2k2dimk𝔟,d^{\mathfrak{b},\bullet}_{k}=2^{k-2}-\dim\mathcal{E}^{\mathfrak{b},\bullet}_{k} are shown in Table 4. In general,

dk𝔟,KNT\displaystyle d^{\mathfrak{b},\mathrm{KNT}}_{k} >\displaystyle> dk𝔟,MJPO>dk.\displaystyle d^{\mathfrak{b},\mathrm{MJPO}}_{k}\,>\,d_{k}.

We can find that the sequence (dk𝔟,MJPO)0k22(d^{\mathfrak{b},\mathrm{MJPO}}_{k})_{0\leq k\leq 22} has a quasi Fibonacci-like rule,

dk𝔟,MJPO\displaystyle d^{\mathfrak{b},\mathrm{MJPO}}_{k} =\displaystyle= dk2𝔟,MJPO+dk3𝔟,MJPO+δM,k,\displaystyle d^{\mathfrak{b},\mathrm{MJPO}}_{k-2}+d^{\mathfrak{b},\mathrm{MJPO}}_{k-3}+\delta_{M,k}, (3.8)

where M={7,15}M=\{7,15\} and δM,k\delta_{M,k} is the Kronecker delta function defined by δM,k=1\delta_{M,k}=1 if kMk\in M and δM,k=0\delta_{M,k}=0 otherwise. It appears that (dk𝔟,KNT)0k22(d^{\mathfrak{b},\mathrm{KNT}}_{k})_{0\leq k\leq 22} does not have an obvious law.

4 Computer program

Our computer programs, that perform the Gaussian forward elimination on the linear combinations in k𝔟\mathcal{E}^{\mathfrak{b}}_{k}, show the following proposition.

Proposition 4.1.

Let kk and rr be a weight and depth, respectively, with r<k22r<k\leq 22. For a mult-index 𝐤{\bf k} in 𝐈k,r{\bf I}_{k,r}, the following statements hold.
(i) If 𝐤𝐈k,rH{\bf k}\notin{\bf I}^{H}_{k,r}, there exists a combination ck,r𝔟k𝔟c\in\mathcal{H}^{\mathfrak{b}}_{k,r}\cap\mathcal{E}^{\mathfrak{b}}_{k} such that

η𝔟(𝐤)\displaystyle\eta^{\mathfrak{b}}({\bf k}) \displaystyle\in c+η𝔟(𝐡)|𝐡𝐈k,rH𝐈k,r1H𝐈k,k/3H𝔽2.\displaystyle c+\langle\eta^{\mathfrak{b}}({\bf h}){\,|\,}{\bf h}\in{\bf I}^{H}_{k,r}\cup{\bf I}^{H}_{k,r-1}\cup\cdots\cup{\bf I}^{H}_{k,\lfloor k/3\rfloor}\rangle_{\mathbb{F}_{2}}. (4.1)

(ii) If 𝐤𝐈k,rH{\bf k}\in{\bf I}^{H}_{k,r}, there exists no combination cc such as (4.1)\mathrm{(\ref{4_PRP1_IncBHS})}.

Here \lfloor\cdot\rfloor is the floor function defined by t=max{a|at}\lfloor t\rfloor=\max\left\{a\in\mathbb{Z}{\,|\,}a\leq t\right\} for a real number tt.

Proposition 4.1 verifies Experiment 3.2. Suppose 𝐤𝐈k,r𝐈k,rH{\bf k}\in{\bf I}_{k,r}\setminus{\bf I}^{H}_{k,r}. By the statement (i),

ζ𝔟(𝐤)\displaystyle\zeta^{\mathfrak{b}}({\bf k}) \displaystyle\in ζ𝔟(𝐡)|𝐡𝐈k,rH𝐈k,r1H𝐈k,k/3H𝔽2,\displaystyle\langle\zeta^{\mathfrak{b}}({\bf h}){\,|\,}{\bf h}\in{\bf I}^{H}_{k,r}\cup{\bf I}^{H}_{k,r-1}\cup\cdots\cup{\bf I}^{H}_{k,\lfloor k/3\rfloor}\rangle_{\mathbb{F}_{2}}, (4.2)

or

ζ¯𝔟(𝐤)\displaystyle\overline{\zeta}^{\mathfrak{b}}({\bf k}) \displaystyle\in ζ¯𝔟(𝐡)|𝐡𝐈k,rH𝔽2,\displaystyle\langle\overline{\zeta}^{\mathfrak{b}}({\bf h}){\,|\,}{\bf h}\in{\bf I}^{H}_{k,r}\rangle_{\mathbb{F}_{2}}, (4.3)

which, together with the statement (ii), implies ζ¯𝔟(𝐈k,rH)\overline{\zeta}^{\mathfrak{b}}({\bf I}^{H}_{k,r}) is a basis of 𝒵¯k,r𝔟\overline{\mathcal{Z}}_{k,r}^{\mathfrak{b}} for r<k22r<k\leq 22.

Imaginarily, the Gaussian elimination can elucidate any vector space whose corresponding matrix (or set of defining linear combinations) is clearly given: but practically, it is limited to a space that are not too big. The bound k=22k=22 in Proposition 4.1 indicates a performance threshold of our computing environments. Below we will describe the environments and prove Proposition 4.1.

The programs are written almost by Python language and partly by Cython language. The machine is as follows: a Linux-based PC having two CPUs with 1212-core at 2.70GHz (Intel Xeon Gold 6226) and a 33TB RAM. The package of the programs is available at https://github.com/machide-tomoyan/BMZS-calculator.

The executable files are in the directories named as 𝙼𝚊𝚒𝚗_𝚖𝚊𝚔𝚎\mathtt{Main\_make} and 𝙼𝚊𝚒𝚗_𝚌𝚊𝚕\mathtt{Main\_cal}. The former contains five files that produce datas of binary systems (or binary matrices) obtained from the binary EDS relations, and the latter contains one file that calculates dimensions of 𝒵k𝔟\mathcal{Z}_{k}^{\mathfrak{b}} and 𝒵¯k,r𝔟\overline{\mathcal{Z}}_{k,r}^{\mathfrak{b}} (or row echelon forms of the corresponding binary matrices). The produced datas are stocked in 𝙳𝚊𝚝𝚊\mathtt{Data}, almost of which are saved in Python pickle format to reduce data size. Class files in which essential precesses are performed are stored in 𝚆𝚘𝚛𝚔\mathtt{Work}. Files of config, license and readme are also placed in the root directory of the package. (See Figure 1 for a layout of the package).

Figure 1: Layout of our package for the executable files.
Refer to caption

We have a convenient expression for a linear combination in 𝒵k𝔟\mathcal{Z}_{k}^{\mathfrak{b}} since 𝔽2\mathbb{F}_{2} consists of only two elements. A subset 𝐉{\bf J} in 𝐈k{\bf I}_{k} is identified with a combination such as

𝐉𝐤𝐉ζ𝔟(𝐤).\displaystyle{\bf J}\quad\,\longleftrightarrow\,\quad\sum_{{\bf k}\in{\bf J}}\zeta^{\mathfrak{b}}({\bf k}). (4.4)

For instance, 𝐉1={(2,1,1),(2,2),(3,1)}{\bf J}_{1}=\{(2,1,1),(2,2),(3,1)\} corresponds to ζ𝔟(2,1,1)+ζ𝔟(2,2)+ζ𝔟(3,1)\zeta^{\mathfrak{b}}(2,1,1)+\zeta^{\mathfrak{b}}(2,2)+\zeta^{\mathfrak{b}}(3,1) and 𝐉2={(2,2),(3,1),(4)}{\bf J}_{2}=\{(2,2),(3,1),(4)\} corresponds to ζ𝔟(2,2)+ζ𝔟(3,1)+ζ𝔟(4)\zeta^{\mathfrak{b}}(2,2)+\zeta^{\mathfrak{b}}(3,1)+\zeta^{\mathfrak{b}}(4). By (4.4), the symmetric difference \bigtriangleup of two sets is equivalent to the plus of two combinations: 𝐉1𝐉2=(𝐉1𝐉2)(𝐉2𝐉1)={(2,1,1),(4)}{\bf J}_{1}\bigtriangleup{\bf J}_{2}=({\bf J}_{1}\setminus{\bf J}_{2})\cup({\bf J}_{2}\setminus{\bf J}_{1})=\{(2,1,1),(4)\} corresponds to (ζ𝔟(2,2)+ζ𝔟(3,1)+ζ𝔟(4))+(ζ𝔟(2,1,1)+ζ𝔟(2,2)+ζ𝔟(3,1))=ζ𝔟(2,1,1)+ζ𝔟(4)(\zeta^{\mathfrak{b}}(2,2)+\zeta^{\mathfrak{b}}(3,1)+\zeta^{\mathfrak{b}}(4))+(\zeta^{\mathfrak{b}}(2,1,1)+\zeta^{\mathfrak{b}}(2,2)+\zeta^{\mathfrak{b}}(3,1))=\zeta^{\mathfrak{b}}(2,1,1)+\zeta^{\mathfrak{b}}(4). The expression (4.4) is also applied to a linear relation 𝐤𝐉ζ𝔟(𝐤)=0\sum_{{\bf k}\in{\bf J}}\zeta^{\mathfrak{b}}({\bf k})=0 in the same way. We compute binary EDS relations (or defining combinations in k𝔟\mathcal{E}^{\mathfrak{b}}_{k}) through (4.4) with the set datatype in Python. This set based expression can be realized by built-in objects.666 We use frozenset and s.symmetric_difference(t) (or the operator notation ‘s^t’), where frozenset is an immutable datatype for set datas and s,t are its instances.

We will explain the executable files and report their statics. We do not mention actual command lines to use the files in a linux OS, but we can find them in the beginning of each file.

4.1 Executable file in 𝙼𝚊𝚒𝚗_𝚖𝚊𝚔𝚎\mathtt{Main\_make}

We will require many maps to save midway datas for the binary linear systems induced from the binary EDS relations. The prime reason is that, by (2.6), each EDS relation is composed of a combination of sh\mathcyr{sh}, * and regsh\mathrm{reg}_{\mathcyr{sh}}. For the maps or the midway datas, we will use dictionary datatype, which is a built-in object in Python and consists of a collection of tuples of two objects called ‘key’ and ‘value’: a key-object is mapped to its associated value-object.

The file 𝟶_𝚙𝚛𝚎𝚙𝚊𝚛𝚊𝚝𝚒𝚘𝚗.𝚙𝚢\mathtt{0\_preparation.py} prepares two dictionary datas for each weight k22k\leq 22. Let [n][n] denote the set {1,,n}\{1,\ldots,n\} for a positive integer nn. One data gives a one-to-one mapping from the integers in [2k][2^{k}] to the words of degree kk, and another data gives a one-to-one mapping from the integers in [2k1][2^{k-1}] to the mult-indices in r=1kr{\textstyle\bigcup_{r=1}^{k}}\mathbb{N}^{r} of weight kk: if n[2k1]n\in[2^{k-1}] and the associated mult-index is 𝐤{\bf k}, the associated word is z𝐤z_{{\bf k}}. The objects of the set which our programs select for the set based expression in the left of (4.4) are the integers (for which the integer datatype is necessary) instead of the mult-indices and words (for which the tuple and string datatypes are necessary), because the integer datatype is reasonable in data size and running time.

The file 𝟷_𝚙𝚛𝚘𝚍𝚞𝚌𝚝.𝚙𝚢\mathtt{1\_product.py} creates dictionary datas for shuffle and stuffle products. The defining equations (2.2) and (2.3) suggest that creating datas of shuffle will take more time since shuffle products can contain more terms. For a speed-up, we improve (2.2):

a1amshb1bn\displaystyle a_{1}\cdots a_{m}\;\mathcyr{sh}\;b_{1}\cdots b_{n} (4.5)
=\displaystyle= i+j=l(0imin{l,m}0jmin{l,n})(a1aishb1bj)(ai+1amshbj+1bn),\displaystyle\sum_{{i+j=l}\atop\left(0\leq i\leq\min\left\{l,m\right\}\atop 0\leq j\leq\min\left\{l,n\right\}\right)}\left(a_{1}\cdots a_{i}\;\mathcyr{sh}\;b_{1}\cdots b_{j}\right)\left(a_{i+1}\cdots a_{m}\;\mathcyr{sh}\;b_{j+1}\cdots b_{n}\right),

where a1,,am,b1,,bn{x,y}a_{1},\ldots,a_{m},b_{1},\ldots,b_{n}\in\{x,y\} and 0<lm+n0<l\leq m+n. This is a spacial case of (2.2) if l=1l=1 and can be proved by induction on ll. Let k=m+nk=m+n. Using (4.5) with l=k/2l=\lfloor k/2\rfloor, we can reduce shuffle products of weight kk to combinations of those of about half weight. We denote by 𝚂𝚑\mathtt{Sh} and 𝚂𝚝\mathtt{St} created datas of shuffle and stuffle, respectively. They map pairs of mult-indices to combinations including temporal indeterminates ζsh𝔟(1,,1,𝐧){\zeta}_{\mathcyr{sh}}^{\mathfrak{b}}(1,\ldots,1,{\bf n}) (𝐧𝐈{})({\bf n}\in{\bf I}\setminus\{\varnothing\}) that are binary versions of regularized MZVs. For instance,

𝚂𝚑((1),(2))=ζsh𝔟(1,2),𝚂𝚝((1),(2))=ζsh𝔟(1,2)+ζ𝔟(2,1)+ζ𝔟(3),𝚂𝚑((1,1),(2))=ζsh𝔟(1,1,2)+ζ𝔟(2,1,1),𝚂𝚝((1,1),(2))=ζsh𝔟(1,1,2)+ζsh𝔟(1,2,1)+ζ𝔟(2,1,1)+ζsh𝔟(1,3)+ζ𝔟(3,1).\begin{split}\mathtt{Sh}((1),(2))&\,=\,{\zeta}_{\mathcyr{sh}}^{\mathfrak{b}}(1,2),\\ \mathtt{St}((1),(2))&\,=\,{\zeta}_{\mathcyr{sh}}^{\mathfrak{b}}(1,2)+\zeta^{\mathfrak{b}}(2,1)+\zeta^{\mathfrak{b}}(3),\\ \mathtt{Sh}((1,1),(2))&\,=\,{\zeta}_{\mathcyr{sh}}^{\mathfrak{b}}(1,1,2)+\zeta^{\mathfrak{b}}(2,1,1),\\ \mathtt{St}((1,1),(2))&\,=\,{\zeta}_{\mathcyr{sh}}^{\mathfrak{b}}(1,1,2)+{\zeta}_{\mathcyr{sh}}^{\mathfrak{b}}(1,2,1)+\zeta^{\mathfrak{b}}(2,1,1)+{\zeta}_{\mathcyr{sh}}^{\mathfrak{b}}(1,3)+\zeta^{\mathfrak{b}}(3,1).\end{split} (4.6)

In the case of shuffle, for using (4.5) with l=k/2l=\lfloor k/2\rfloor, we also create maps from pairs of words to combinations of words up to weight 22/2=1122/2=11. In those additional maps, we allow the words that can not be written in terms of mult-indices (e.g., xx and yx=z1xyx=z_{1}x).

Let 𝐤{\bf k} be a mult-index that is expressed as z𝐤=ynz𝐧z_{{\bf k}}=y^{n}z_{{\bf n}}, where n0n\geq 0 and 𝐧𝐈{}{\bf n}\in{\bf I}\setminus\{\varnothing\}. Let 𝐧{\bf n}^{\prime} denote a mult-index such that z𝐧=xz𝐧z_{{\bf n}}=xz_{{\bf n}^{\prime}}. By [19, Proposition 8],

regsh(ynz𝐧)\displaystyle\mathrm{reg}_{\mathcyr{sh}}(y^{n}z_{{\bf n}}) =\displaystyle= (1)nx(ynshz𝐧).\displaystyle(-1)^{n}x(y^{n}\;\mathcyr{sh}\;z_{{\bf n}^{\prime}}). (4.7)

Since the regularized MZV of z𝐤z_{{\bf k}} is Zregsh(ynz𝐧)Z\circ\mathrm{reg}_{\mathcyr{sh}}(y^{n}z_{{\bf n}}), its binary version should be

ζsh𝔟(𝐤)\displaystyle{\zeta}_{\mathcyr{sh}}^{\mathfrak{b}}({\bf k}) =\displaystyle= Z𝔟regsh(ynz𝐧)=H𝔟can𝔟(x(ynshz𝐧)).\displaystyle Z^{\mathfrak{b}}\circ\mathrm{reg}_{\mathcyr{sh}}(y^{n}z_{{\bf n}})\,=\,H^{\mathfrak{b}}\circ\mathrm{can}^{\mathfrak{b}}(x(y^{n}\;\mathcyr{sh}\;z_{{\bf n}^{\prime}})). (4.8)

The dictionary datas that the file 𝟸_𝚛𝚎𝚐𝚞𝚕𝚊𝚛𝚒𝚣𝚎𝚍_𝚙𝚛𝚘𝚍𝚞𝚌𝚝.𝚙𝚢\mathtt{2\_regularized\_product.py} creates are obtained by applying (4.8) to ones that the previous file creates. For instance, we have by (4.8)

ζsh𝔟(1,2)\displaystyle{\zeta}_{\mathcyr{sh}}^{\mathfrak{b}}(1,2) =\displaystyle= H𝔟can𝔟(x(yshy))=H𝔟(can𝔟(2xyy))= 0,\displaystyle H^{\mathfrak{b}}\circ\mathrm{can}^{\mathfrak{b}}(x(y\;\mathcyr{sh}\;y))\,=\,H^{\mathfrak{b}}(\mathrm{can}^{\mathfrak{b}}(2xyy))\,=\,0,
ζsh𝔟(1,1,2)\displaystyle\rule{0.0pt}{15.0pt}{\zeta}_{\mathcyr{sh}}^{\mathfrak{b}}(1,1,2) =\displaystyle= H𝔟can𝔟(x(y2shy))=H𝔟(can𝔟(3xyyy))=ζ𝔟(2,1,1),\displaystyle H^{\mathfrak{b}}\circ\mathrm{can}^{\mathfrak{b}}(x(y^{2}\;\mathcyr{sh}\;y))\,=\,H^{\mathfrak{b}}(\mathrm{can}^{\mathfrak{b}}(3xyyy))\,=\,\zeta^{\mathfrak{b}}(2,1,1),
ζsh𝔟(1,2,1)\displaystyle\rule{0.0pt}{15.0pt}{\zeta}_{\mathcyr{sh}}^{\mathfrak{b}}(1,2,1) =\displaystyle= H𝔟can𝔟(x(yshyy))=H𝔟(can𝔟(3xyyy))=ζ𝔟(2,1,1),\displaystyle H^{\mathfrak{b}}\circ\mathrm{can}^{\mathfrak{b}}(x(y\;\mathcyr{sh}\;yy))\,=\,H^{\mathfrak{b}}(\mathrm{can}^{\mathfrak{b}}(3xyyy))\,=\,\zeta^{\mathfrak{b}}(2,1,1),
ζsh𝔟(1,3)\displaystyle\rule{0.0pt}{15.0pt}{\zeta}_{\mathcyr{sh}}^{\mathfrak{b}}(1,3) =\displaystyle= H𝔟can𝔟(x(yshxy))=H𝔟(can𝔟(xyxy+2xxyy))=ζ𝔟(2,2),\displaystyle H^{\mathfrak{b}}\circ\mathrm{can}^{\mathfrak{b}}(x(y\;\mathcyr{sh}\;xy))\,=\,H^{\mathfrak{b}}(\mathrm{can}^{\mathfrak{b}}(xyxy+2xxyy))\,=\,\zeta^{\mathfrak{b}}(2,2),

and so the previous datas in (4.6) are converted to

𝚂𝚑sh((1),(2))= 0,𝚂𝚝sh((1),(2))=ζ𝔟(2,1)+ζ𝔟(3),𝚂𝚑sh((1,1),(2))= 0,𝚂𝚝sh((1,1),(2))=ζ𝔟(2,1,1)+ζ𝔟(2,2)+ζ𝔟(3,1),\begin{split}\mathtt{Sh}_{\mathcyr{sh}}((1),(2))&\,=\,0,\\ \mathtt{St}_{\mathcyr{sh}}((1),(2))&\,=\,\zeta^{\mathfrak{b}}(2,1)+\zeta^{\mathfrak{b}}(3),\\ \mathtt{Sh}_{\mathcyr{sh}}((1,1),(2))&\,=\,0,\\ \mathtt{St}_{\mathcyr{sh}}((1,1),(2))&\,=\,\zeta^{\mathfrak{b}}(2,1,1)+\zeta^{\mathfrak{b}}(2,2)+\zeta^{\mathfrak{b}}(3,1),\end{split} (4.9)

where 𝚂𝚑sh\mathtt{Sh}_{\mathcyr{sh}} and 𝚂𝚝sh\mathtt{St}_{\mathcyr{sh}} stand for the maps of regularized shuffle and stuffle, respectively.

The file 𝟹_𝚎𝚡𝚝𝚎𝚗𝚍𝚎𝚍_𝚛𝚎𝚕𝚊𝚝𝚒𝚘𝚗.𝚙𝚢\mathtt{3\_extended\_relation.py} makes binary EDS relations,

𝚂𝚝sh(𝐤,𝐥)+𝚂𝚑sh(𝐤,𝐥)\displaystyle\mathtt{St}_{\mathcyr{sh}}({\bf k},{\bf l})+\mathtt{Sh}_{\mathcyr{sh}}({\bf k},{\bf l}) =\displaystyle= 0((𝐤,𝐥)𝐏𝐈^k),\displaystyle 0\qquad(({\bf k},{\bf l})\in\widehat{{\bf PI}}_{k}),

by combining the previous datas. For instance, the datas in (4.9) create the two relations,

ζ𝔟(2,1)+ζ𝔟(3)\displaystyle\zeta^{\mathfrak{b}}(2,1)+\zeta^{\mathfrak{b}}(3) =\displaystyle= 0,\displaystyle 0,
ζ𝔟(2,1,1)+ζ𝔟(2,2)+ζ𝔟(3,1)\displaystyle\zeta^{\mathfrak{b}}(2,1,1)+\zeta^{\mathfrak{b}}(2,2)+\zeta^{\mathfrak{b}}(3,1) =\displaystyle= 0.\displaystyle 0.

The file 𝟺_𝚋𝚒𝚗𝚊𝚛𝚢_𝚜𝚢𝚜𝚝𝚎𝚖.𝚙𝚢\mathtt{4\_binary\_system.py} converts the binary EDS relations of a weight kk to a binary linear system (which we call a binary EDS linear system) in both of text and pickle formats. The text format is organized as follows:

  1. 1.

    A line with the first character ‘#’ is a comment line. Comment lines typically occur at the beginning of the file, but are allowed to appear throughout the file.

  2. 2.

    The remainder of the file contains lines defining the binary linear relations, one by one.

  3. 3.

    A relation is defined by positive integers numbering binary MZSs. A number ‘0’ is typically placed at the last of the line, but it is optional.

For example, the line “2 4 0” is corresponding to ζ𝔟(2,1)+ζ𝔟(3)=0\zeta^{\mathfrak{b}}(2,1)+\zeta^{\mathfrak{b}}(3)=0, if ζ𝔟(2,1)\zeta^{\mathfrak{b}}(2,1) and ζ𝔟(3)\zeta^{\mathfrak{b}}(3) are numbered as 22 and 44, respectively. The pickle files are not necessary but useful: e.g., when loading the large size system. For Experiment 3.3, we also make binary KNT\mathrm{KNT} and MJPO\mathrm{MJPO} linear systems by restricting binary EDS relations.

The above programs run under the parallel process since the datas can be created independently if 𝐏𝐈^k\widehat{{\bf PI}}_{k} is divided into a plurality of blocks. The filenames of the datas by the parallel process have strings ‘_𝙱𝚗\_\mathtt{Bn}(𝚗)(\mathtt{n}\in\mathbb{N}) at their tails. Editing the file 𝚌𝚘𝚗𝚏𝚒𝚐.𝚝𝚡𝚝\mathtt{config.txt} we can control the max number of parallel threads.

In Table 5, we present computation times (or elapsed real times) to execute all files for k18k\geq 18, where 𝚒_𝙼.𝚙𝚢\mathtt{i\_M.py} stands for the ii-th file mentioned above from 0 to 44. We find that calculating the regularizations in 𝟸_𝙼.𝚙𝚢\mathtt{2\_M.py} is the dominant process. Table 6 lists the file sizes of the binary linear systems in pickle format for 𝐏𝐈^kKNT\widehat{{\bf PI}}_{k}^{\mathrm{KNT}}, 𝐏𝐈^kMJPO\widehat{{\bf PI}}_{k}^{\mathrm{MJPO}} and 𝐏𝐈^k=𝐏𝐈^kEDS\widehat{{\bf PI}}_{k}=\widehat{{\bf PI}}_{k}^{\mathrm{EDS}}. As expected from 𝐏𝐈^kKNT𝐏𝐈^kMJPO𝐏𝐈^k\widehat{{\bf PI}}_{k}^{\mathrm{KNT}}\subset\widehat{{\bf PI}}_{k}^{\mathrm{MJPO}}\subset\widehat{{\bf PI}}_{k}, the file of KNT\mathrm{KNT} is smallest and that of EDS is largest for each weight. The size of text format file is about 1.51.5 times the size of pickle one. For each weight kk, the maximum memory size (or maximum resident set size) to execute the files 𝚒_𝙼.𝚙𝚢\mathtt{i\_M.py} is the size required by 𝟺_𝙼.𝚙𝚢\mathtt{4\_M.py}, which is about half size used in Gaussian forward elimination (see Table 7). Computationally, making linear systems is not harder than calculating their coranks (or dimensions of cokernel) as we will see below.

Table 5: Elapsed real times [sec] to make binary systems.
kk 𝟶_𝙼.𝚙𝚢\mathtt{0\_M.py} 𝟷_𝙼.𝚙𝚢\mathtt{1\_M.py} 𝟸_𝙼.𝚙𝚢\mathtt{2\_M.py} 𝟹_𝙼.𝚙𝚢\mathtt{3\_M.py} 𝟺_𝙼.𝚙𝚢\mathtt{4\_M.py} Total
18 0 75 246 53 54 428 (\fallingdotseq 7min)
19 1 188 805 133 186 1313 (\fallingdotseq 22min)
20 3 469 3137 510 543 4662 (\fallingdotseq 1.3hour)
21 7 1529 15607 1384 2362 20889 (\fallingdotseq 5.8hour)
22 15 3018 61898 3675 6578 75184 (\fallingdotseq 21hour)
Table 6: File sizes of binary linear systems in pickle format.
kk KNT\mathrm{KNT} MJPO\mathrm{MJPO} EDS
18 8.3M 150M 274M
19 21M 509M 922M
20 47M 1.6G 2.8G
21 105M 5G 8.6G
22 233M 16G 26G

4.2 Executable file in 𝙼𝚊𝚒𝚗_𝚌𝚊𝚕\mathtt{Main\_cal}

The file 𝚍𝚒𝚖𝚎𝚗𝚜𝚒𝚘𝚗𝚜.𝚙𝚢\mathtt{dimensions.py} (𝚍_𝙲.𝚙𝚢\mathtt{d\_C.py} for short) executes the Gaussian forward elimination on a given binary linear system of a weight kk by using Algorithm A.6. In the process, an order of mult-indices (or binary MZSs) have to be determined to convert the inputted binary linear system into the corresponding binary matrix. We employ a sequence (𝐤1,,𝐤2k2)({{\bf k}_{1}},\ldots,{{\bf k}_{2^{k-2}}}) satisfying the following: if i<ji<j,

  • (a)

    d(𝐤i)>d(𝐤j){\mathrm{d}}({\bf k}_{i})>{\mathrm{d}}({\bf k}_{j}); or

  • (b)

    d(𝐤i)=d(𝐤j){\mathrm{d}}({\bf k}_{i})={\mathrm{d}}({\bf k}_{j}) and (𝐤i,𝐤j)𝐈kH×(𝐈k𝐈kH)({\bf k}_{i},{\bf k}_{j})\notin{\bf I}^{H}_{k}\times({\bf I}_{k}\setminus{\bf I}^{H}_{k}).

The condition (a) means that the mult-indices (or columns in the corresponding matrix) are sectioned into k1k-1 blocks by depth: the mult-indices in a left block have a greater depth than those in a right block. The condition (b) means that the Hoffman mult-indices of depth rr are at the rightmost place in the (kr)(k-r)th block. For example, the order of weight 44 determined by 𝐤1=(2,1,1){\bf k}_{1}=(2,1,1), 𝐤2=(3,1){\bf k}_{2}=(3,1), 𝐤3=(2,2){\bf k}_{3}=(2,2) and 𝐤4=(4){\bf k}_{4}=(4) satisfies (a) and (b): they are sectioned as (𝐤1)|(𝐤2,𝐤3)|(𝐤4)({\bf k}_{1})|({\bf k}_{2},{\bf k}_{3})|({\bf k}_{4}) and the only Hoffman mult-index 𝐤3{\bf k}_{3} is located rightmost in the 22th block.

Proposition 4.1 is shown as follows.


Proof of Proposition 4.1.  We consider the situation where we run 𝚍_𝙲.𝚙𝚢\mathtt{d\_C.py} by inputing the binary EDS linear system of weight kk. We then obtain a row echelon matrix satisfies the following.

  • (E1)

    There exists a non-zero pivot at any column 𝐤{\bf k} in 𝐈k𝐈kH{\bf I}_{k}\setminus{\bf I}^{H}_{k}.

  • (E2)

    There exists no non-zero pivot at any column 𝐤{\bf k} in 𝐈kH{\bf I}^{H}_{k}.

For a non-zero combination c=η𝔟(𝐤i1)++η𝔟(𝐤ij)c=\eta^{\mathfrak{b}}({\bf k}_{i_{1}})+\cdots+\eta^{\mathfrak{b}}({\bf k}_{i_{j}}) in k𝔟\mathcal{H}^{\mathfrak{b}}_{k} with i1<<iji_{1}<\cdots<i_{j}, we define the leading term of cc by

L(c)\displaystyle L(c) =\displaystyle= η𝔟(𝐤i1).\displaystyle\eta^{\mathfrak{b}}({\bf k}_{i_{1}}).

By (a) and (b), the statements (E1) and (E2) are equivalent to (e1) and (e2), respectively:

  • (e1)

    There exists a combination ck𝔟c\in\mathcal{E}^{\mathfrak{b}}_{k} such that L(c)=η𝔟(𝐤)L(c)=\eta^{\mathfrak{b}}({\bf k}) for any 𝐤{\bf k} in 𝐈k𝐈kH{\bf I}_{k}\setminus{\bf I}^{H}_{k}.

  • (e2)

    There exists no combination ck𝔟c\in\mathcal{E}^{\mathfrak{b}}_{k} such that L(c)=η𝔟(𝐤)L(c)=\eta^{\mathfrak{b}}({\bf k}) for any 𝐤{\bf k} in 𝐈kH{\bf I}^{H}_{k}.

Under (e1) and (e2), the back substitution (performed imaginarily) implies Proposition 4.1, where the fact that 𝐈k,rH=ϕ{\bf I}^{H}_{k,r}=\phi for any depth r<k/3r<\lfloor k/3\rfloor is used for (4.1). \Box

We give examples of (4.3) for k7k\leq 7 excluding the case that ζ¯𝔟(𝐤)=0\overline{\zeta}^{\mathfrak{b}}({\bf k})=0. Note that ζ¯𝔟(𝐤)\overline{\zeta}^{\mathfrak{b}}({\bf k}) is always zero if 𝐤𝐈k,r{\bf k}\in{\bf I}_{k,r} and 𝐈k,rH=ϕ{\bf I}^{H}_{k,r}=\phi.

ζ¯𝔟(3,1)\displaystyle\overline{\zeta}^{\mathfrak{b}}(3,1) =\displaystyle= ζ¯𝔟(2,2),\displaystyle\overline{\zeta}^{\mathfrak{b}}(2,2),
ζ¯𝔟(4,1)\displaystyle\rule{0.0pt}{15.0pt}\rule{0.0pt}{25.0pt}\overline{\zeta}^{\mathfrak{b}}(4,1) =\displaystyle= ζ¯𝔟(2,3)+ζ¯𝔟(3,2),\displaystyle\overline{\zeta}^{\mathfrak{b}}(2,3)+\overline{\zeta}^{\mathfrak{b}}(3,2),
ζ¯𝔟(2,1,3)=ζ¯𝔟(3,2,1)=ζ¯𝔟(4,1,1)\displaystyle\rule{0.0pt}{15.0pt}\rule{0.0pt}{25.0pt}\overline{\zeta}^{\mathfrak{b}}(2,1,3)\,=\,\overline{\zeta}^{\mathfrak{b}}(3,2,1)\,=\,\overline{\zeta}^{\mathfrak{b}}(4,1,1) =\displaystyle= ζ¯𝔟(2,2,2),\displaystyle\overline{\zeta}^{\mathfrak{b}}(2,2,2),
ζ¯𝔟(5,1)\displaystyle\rule{0.0pt}{15.0pt}\overline{\zeta}^{\mathfrak{b}}(5,1) =\displaystyle= ζ¯𝔟(3,3),\displaystyle\overline{\zeta}^{\mathfrak{b}}(3,3),
ζ¯𝔟(5,1,1)=ζ¯𝔟(3,1,3)\displaystyle\rule{0.0pt}{15.0pt}\rule{0.0pt}{25.0pt}\overline{\zeta}^{\mathfrak{b}}(5,1,1)\,=\,\overline{\zeta}^{\mathfrak{b}}(3,1,3) =\displaystyle= ζ¯𝔟(2,2,3)+ζ¯𝔟(2,3,2)+ζ¯𝔟(3,2,2),\displaystyle\overline{\zeta}^{\mathfrak{b}}(2,2,3)+\overline{\zeta}^{\mathfrak{b}}(2,3,2)+\overline{\zeta}^{\mathfrak{b}}(3,2,2),
ζ¯𝔟(3,3,1)\displaystyle\rule{0.0pt}{15.0pt}\overline{\zeta}^{\mathfrak{b}}(3,3,1) =\displaystyle= ζ¯𝔟(2,3,2),\displaystyle\overline{\zeta}^{\mathfrak{b}}(2,3,2),
ζ¯𝔟(4,2,1)\displaystyle\rule{0.0pt}{15.0pt}\overline{\zeta}^{\mathfrak{b}}(4,2,1) =\displaystyle= ζ¯𝔟(2,2,3)+ζ¯𝔟(2,3,2),\displaystyle\overline{\zeta}^{\mathfrak{b}}(2,2,3)+\overline{\zeta}^{\mathfrak{b}}(2,3,2),
ζ¯𝔟(4,1,2)=ζ¯𝔟(2,1,4)\displaystyle\rule{0.0pt}{15.0pt}\overline{\zeta}^{\mathfrak{b}}(4,1,2)\,=\,\overline{\zeta}^{\mathfrak{b}}(2,1,4) =\displaystyle= ζ¯𝔟(2,2,3)+ζ¯𝔟(3,2,2),\displaystyle\overline{\zeta}^{\mathfrak{b}}(2,2,3)+\overline{\zeta}^{\mathfrak{b}}(3,2,2),
ζ¯𝔟(2,4,1)\displaystyle\rule{0.0pt}{15.0pt}\overline{\zeta}^{\mathfrak{b}}(2,4,1) =\displaystyle= ζ¯𝔟(2,3,2)+ζ¯𝔟(3,2,2).\displaystyle\overline{\zeta}^{\mathfrak{b}}(2,3,2)+\overline{\zeta}^{\mathfrak{b}}(3,2,2).

Examining the Gaussian forward elimination performed by 𝚍_𝙲.𝚙𝚢\mathtt{d\_C.py} in detail, we can find a part of the inputted binary EDS relations which forms a basis of k𝔟\mathcal{E}^{\mathfrak{b}}_{k}. We give examples of bases for k6k\leq 6, where only the pairs of mult-indices are written (see Table 1 that lists associated relations for k4k\leq 4).

  • k=3k=3

    ((1),(2))((1),(2)).

  • k=4k=4

    ((1),(2,1))((1),(2,1)), ((1),(3))((1),(3)), ((2),(2))((2),(2)).

  • k=5k=5

    ((1),(2,1,1))((1),(2,1,1)), ((1),(2,2))((1),(2,2)), ((1),(3,1))((1),(3,1)), ((1),(4))((1),(4)), ((2),(2,1))((2),(2,1)), ((2),(3))((2),(3)).

  • k=6k=6

    ((1),(2,1,1,1))((1),(2,1,1,1)), ((1),(2,1,2))((1),(2,1,2)), ((1),(2,2,1))((1),(2,2,1)), ((1),(2,3))((1),(2,3)), ((1),(3,1,1))((1),(3,1,1)), ((1),(3,2))((1),(3,2)), ((1),(4,1))((1),(4,1)), ((1),(5))((1),(5)), ((2),(2,1,1))((2),(2,1,1)), ((2),(2,2))((2),(2,2)), ((2),(3,1))((2),(3,1)), ((2,1),(2,1))((2,1),(2,1)), ((2,1),(3))((2,1),(3)), ((3),(3))((3),(3)).

We can verify Experiment 3.3 similarly to Experiment 3.2. We input KNT\mathrm{KNT} and MJPO\mathrm{MJPO} linear systems into 𝚍_𝙲.𝚙𝚢\mathtt{d\_C.py}. By Table 4, in most cases, row echelon matrices that do not satisfy (E1) are outputted. The fails of (E1) induce (3.7), and ensure Experiment 3.3.

The program in 𝚍_𝙲.𝚙𝚢\mathtt{d\_C.py} applies the parallel process to determine an order of mult-indices since mult-indices can be divided by depth. For instance, (k1)(k-1) parallel threads occur as preprocessing if a binary EDS linear system of weight kk is inputted. Algorithm A.6, the main process for computing a row echelon matrix, is executed in single. It appears that the parallelization of Algorithm A.6 is not easy because a non-simple search procedure is incorporated.

In Table 7, we present the statics of the executions by 𝚍_𝙲.𝚙𝚢\mathtt{d\_C.py} whose inputs are the binary KNT\mathrm{KNT}, MJPO\mathrm{MJPO} and EDS relations. We observe that the computation for KNT\mathrm{KNT} requires much more time than MJPO\mathrm{MJPO} and EDS, although the number of relations of KNT\mathrm{KNT} is quite small such that the corresponding matrix is square for any k7k\geq 7. This phenomenon expresses a characteristic of Algorithm A.6. It employs a conflict based search procedure inspired by the conflict-driven clause learning (CDCL), a modern method with many successes to practical applications in solving the Boolean satisfiability (SAT) problem. Roughly speaking, relations with good structures for finding conflict combinations can accelerate searching a pivot relation (see Remark A.7 for more information). The memory cost is bad in comparison with the statics in [21], but the runtime is about 10 times more faster. Therefore we can improve the record of calculating (3.2) from k=20k=20 to 2222 by the use of a machine with large memory capacity.

Table 7: Statistics of the computations of Experiments 3.2 and 3.3. ‘Rels’ is the number of relations. ‘MeanNum’ is the average number of terms per relation. ‘Memory’ and ‘Time’ are the resident set size and elapsed real time, respectively. In each block with respect to the weight kk, top row indicates information on KNT\mathrm{KNT}, middle row indicates that on MJPO\mathrm{MJPO} and bottom row indicates that on EDS.
kk 2k22^{k-2} Rels MeanNum Memory Time
18 65536 65536 30.1 4.6G 8.6hour KNT\mathrm{KNT}
\cdashline3-7 155711 230.4 7.3G 8.8min MJPO\mathrm{MJPO}
\cdashline3-7 188470 364.4 11.4G 9.8min EDS
19 131072 131072 33.7 16.5G 68hour
\cdashline3-6 327679 339.5 22.9G 42.4min
\cdashline3-6 393206 523.1 34.3G 43.7min
20 262144 262144 37.6 61G 22day
\cdashline3-6 688254 500.5 82G 5.3hour
\cdashline3-6 819316 751.7 110G 4.7hour
21 524288 - - - -
\cdashline3-6 1441791 739.8 256G 30hour
\cdashline3-6 1703925 1083.3 329G 25hour
22 1048576 - - - -
\cdashline3-6 3014911 1094.4 789G 8day
\cdashline3-6 3539188 1564.1 982G 7day

5 Problem

Some problems arise in connection with the experiments in Section 3.

Experiments 3.1 and 3.2 indicate typical problems on the dimensions of 𝒵k𝔟\mathcal{Z}_{k}^{\mathfrak{b}} and 𝒵¯k,r𝔟\overline{\mathcal{Z}}_{k,r}^{\mathfrak{b}}: obviously, Problem 5.2 includes Problem 5.1.

Problem 5.1.

Does (3.1) hold for any weight kk?

Problem 5.2.

Does (3.3) (or Proposition 4.1) hold for any weight kk and depth rr?

Experiment 3.3 yields the following:

Problem 5.3.

(i) Is there a subset MM\subset\mathbb{N} such that M[22]={7,15}M\cap[22]=\{7,15\} and the sequence (dk𝔟,MJPO)k0(d^{\mathfrak{b},\mathrm{MJPO}}_{k})_{k\geq 0} satisfies (3.8)?
(ii) Can we find a law in the sequence (dk𝔟,KNT)k0(d^{\mathfrak{b},\mathrm{KNT}}_{k})_{k\geq 0}?

We have adopted the binary field 𝔽2\mathbb{F}_{2} for the scalar field of the formal multiple zeta space and for the computation of corank. (It is worth noting that the experiments of [21] employ 𝔽16381\mathbb{F}_{16381} and 𝔽31991\mathbb{F}_{31991}.) There are no particular reasons for choosing 𝔽2\mathbb{F}_{2} except computational science techniques are easy to apply. A discovery of a regularity of dk,r𝔟d^{\mathfrak{b}}_{k,r} in Table 2 is a product of good luck.

Problem 5.4.

(i) Can we find a theoretical reason why the dimensions dk,r𝔟d^{\mathfrak{b}}_{k,r} (r<k22)(r<k\leq 22) have a Pascal triangle pattern?
(ii) What will the dimensions be if we adopt other finite fields 𝔽p\mathbb{F}_{p}?

Like MZVs, we can make an assumption that binary MZSs satisfy a multiplication compatible with the shuffle and stuffle products. Under the assumption, we have ζ𝔟(2)2=0\zeta^{\mathfrak{b}}(2)^{2}=0 since Z𝔟(z2)Z𝔟(z2)=Z𝔟(z2shz2)=H𝔟can𝔟(2z2,2+4z3,1)=0Z^{\mathfrak{b}}(z_{2})Z^{\mathfrak{b}}(z_{2})=Z^{\mathfrak{b}}(z_{2}\,\mathcyr{sh}\,z_{2})=H^{\mathfrak{b}}\circ\mathrm{can}^{\mathfrak{b}}(2z_{2,2}+4z_{3,1})=0. This means that the algebras of MZV and binary MZS are different. In particular, 𝔽2[ζ𝔟(2)]=1,ζ𝔟(2)𝔽2\mathbb{F}_{2}[\zeta^{\mathfrak{b}}(2)]=\langle 1,\zeta^{\mathfrak{b}}(2)\rangle_{\mathbb{F}_{2}} is not isomorphic to the polynomial ring in one variable, and statements and conjectures involving 𝒵/ζ(2)𝒵\mathcal{Z}/\zeta(2)\mathcal{Z} (e.g., those involving finite and symmetric multiple zeta values introduced in [20]) can not be varied to 𝒵𝔟/ζ𝔟(2)𝒵𝔟\mathcal{Z}^{\mathfrak{b}}/\zeta^{\mathfrak{b}}(2)\mathcal{Z}^{\mathfrak{b}} directly. It seems a mysterious problem that whether the algebra of binary MZS has a good property and a connection to the algebra of MZV.

Acknowledgements

The author would like to thank Tomohiro Sonobe for help with computing environments, and Junichi Teruyama for a recommendation to use (4.5) which made it possible to reduce computation costs. This work was supported by Japan Society for the Promotion of Science, Grant-in-Aid for Scientific Research (C) 20K03727.

Appendix

We will introduce a technique to speed up the Gaussian forward elimination over any field KK for a system of linear combinations that have some structure. An essential part of the technique appears in [24] to decide the full rankness of a binary matrix.

Let x1,,xnx_{1},\ldots,x_{n} be variables, and we order the variables according to their subscripts. For a non-zero linear combination p=p(x1,,xn)=cixip=p(x_{1},\ldots,x_{n})=\sum{}c_{i}x_{i} over KK, we denote by smin(l)s_{\mathrm{min}}(l) and cmin(l)c_{\mathrm{min}}(l) the subscript and coefficient of the minimum variable, respectively. That is, smin(p)=min{i|ci0}s_{\mathrm{min}}(p)=\min\left\{i{\,|\,}c_{i}\neq 0\right\} and cmin(p)=csmin(p)c_{\mathrm{min}}(p)=c_{s_{\mathrm{min}}(p)}. We define smin(p)=n+1s_{\mathrm{min}}(p)=n+1 and cmin(p)=0c_{\mathrm{min}}(p)=0 when p=0p=0.

In what follows we will handle mainly linear combinations over KK, and we just call them combinations. Let 𝒦p1,,pm\mathcal{K}_{p_{1},\ldots,p_{m}} denote the KK-vector space spanned by combinations p1,,pmp_{1},\ldots,p_{m}, and let 𝒦p1,,pm=𝒦p1,,pm{0}\mathcal{K}^{*}_{p_{1},\ldots,p_{m}}=\mathcal{K}_{p_{1},\ldots,p_{m}}\setminus\{0\}. We say that pip_{i} is a pivot combination if smin(pi)=is_{\mathrm{min}}(p_{i})=i, and

(pig)1gh\displaystyle(p_{i_{g}})_{1\leq g\leq h} =\displaystyle= (pi1,,pih)\displaystyle(p_{i_{1}},\ldots,p_{i_{h}})

is a pivot sequence if 1i1<<ihn1\leq i_{1}<\cdots<i_{h}\leq n and every pigp_{i_{g}} is a pivot combination.

There are two key processes for the speed-up technique. One is a conflict search procedure.

Process A.1.

Input: Combinations L={l1,,lm}L=\{l_{1},\ldots,l_{m}\} and a pivot sequence (p1,,pj1)(p_{1},\ldots,p_{j-1}).

Output: Either (0,)(0,\varnothing) or (qi,𝐤i)(q_{i},{\bf k}_{i}) such that

  1. (a)

    qiLq_{i}\in L with smin(qi)=ijs_{\mathrm{min}}(q_{i})=i\leq j;

  2. (b)

    𝐤i=(ki,,kj1,kj,,kn)Kni+1{\bf k}_{i}=(k_{i},\ldots,k_{j-1},k_{j},\ldots,k_{n})\in K^{n-i+1} with kj=1k_{j}=1 and kj+1==kn=0k_{j+1}=\cdots=k_{n}=0;

  3. (c)

    qi|(xi,,xn)=𝐤iKq_{i}{\,|\,}_{(x_{i},\ldots,x_{n})={\bf k}_{i}}\in K^{*}; and

  4. (d)

    pi|(xi,,xn)=𝐤i==pj1|(xi,,xn)=𝐤i=0p_{i}{\,|\,}_{(x_{i},\ldots,x_{n})={\bf k}_{i}}=\cdots=p_{j-1}{\,|\,}_{(x_{i},\ldots,x_{n})={\bf k}_{i}}=0.

  1. 1.

    Set 𝐤j=(kj,,kn)=(1,0,,0)Knj+1{\bf k}_{j}=(k_{j},\ldots,k_{n})=(1,0,\ldots,0)\in K^{n-j+1} and i=ji=j.

  2. 2.

    Search qiq_{i} from {lL|smin(l)=i}\{l\in L{\,|\,}s_{\mathrm{min}}(l)=i\} such that qi|(xi,,xn)=𝐤iKq_{i}{\,|\,}_{(x_{i},\ldots,x_{n})={\bf k}_{i}}\in K^{*}.

  3. 3.

    Return (qi,𝐤i)(q_{i},{\bf k}_{i}) if such qiq_{i} exists.

  4. 4.

    Return (0,)(0,\varnothing) if i=1i=1.

  5. 5.

    Evaluate ki1=pi1cmin(pi1)xi1cmin(pi1)|(xi,,xn)=𝐤iKk_{i-1}=-\dfrac{p_{i-1}-c_{\mathrm{min}}(p_{i-1})x_{i-1}}{c_{\mathrm{min}}(p_{i-1})}{\,\bigg{|}\,}_{(x_{i},\ldots,x_{n})={\bf k}_{i}}\in K.777 This evaluation is well-defined since smin(pi1)=i1s_{\mathrm{min}}(p_{i-1})=i-1 and cmin(pi1)0c_{\mathrm{min}}(p_{i-1})\neq 0. The condition (d) follows from pi1|(xi1,,xn)=(ki1,,kn)\displaystyle p_{i-1}{\,|\,}_{(x_{i-1},\ldots,x_{n})=(k_{i-1},\ldots,k_{n})} =\displaystyle= cmin(pi1)ki1+(pi1cmin(pi1)xi1)|(xi,,xn)=(ki,,kn)= 0.\displaystyle c_{\mathrm{min}}(p_{i-1})k_{i-1}+(p_{i-1}-c_{\mathrm{min}}(p_{i-1})x_{i-1}){\,|\,}_{(x_{i},\ldots,x_{n})=(k_{i},\ldots,k_{n})}\,=\,0.

  6. 6.

    Set 𝐤i1=(ki1,𝐤i){\bf k}_{i-1}=(k_{i-1},{\bf k}_{i}).

  7. 7.

    Update ii1i\leftarrow i-1, and go back to step 22.

Another is the classical elimination procedure with an evidence of conflict.

Process A.2.

Input: A pivot sequence (pi,,pj1)(p_{i},\ldots,p_{j-1}) and a pair (qi,𝐤i)(0,)(q_{i},{\bf k}_{i})\neq(0,\varnothing) which satisfies the output conditions in Process A.1.

Output: A combination qj𝒦qi,pi,,pjq_{j}\in\mathcal{K}^{*}_{q_{i},p_{i},\ldots,p_{j}} such that smin(qj)=js_{\mathrm{min}}(q_{j})=j.888 The theory of Gaussian elimination only ensures qj𝒦qi,pi,,pjq_{j}\in\mathcal{K}_{q_{i},p_{i},\ldots,p_{j}} and smin(qj)js_{\mathrm{min}}(q_{j})\geq j. However, updating method of qq in step 2, together with the output conditions (c) and (d) in Process A.1, implies qj|(xi,,xn)=𝐤iKq_{j}{\,|\,}_{(x_{i},\ldots,x_{n})={\bf k}_{i}}\in K^{*}. It also implies smin(qj)=js_{\mathrm{min}}(q_{j})=j. In fact, if smin(qj)>js_{\mathrm{min}}(q_{j})>j, qj|(xi,,xn)=𝐤i\displaystyle q_{j}{\,|\,}_{(x_{i},\ldots,x_{n})={\bf k}_{i}} =\displaystyle= qj|(xj+1,,xn)=(kj+1,,kn)=qj|xj+1==xn=0= 0,\displaystyle q_{j}{\,|\,}_{(x_{j+1},\ldots,x_{n})=(k_{j+1},\ldots,k_{n})}\,=\,q_{j}{\,|\,}_{x_{j+1}=\cdots=x_{n}=0}\,=\,0, which is a contradiction. Therefore the output condition in Process A.2 holds.

  1. 1.

    Set q=qiq=q_{i}.

  2. 2.

    For hh from ii to j1j-1, update qqcmin(q)cmin(ph)phq\leftarrow q-\dfrac{c_{\mathrm{min}}(q)}{c_{\mathrm{min}}(p_{h})}p_{h} if h=smin(q)h=s_{\mathrm{min}}(q).

  3. 3.

    Return qj=qq_{j}=q.

We can construct a process to find a new pivot combination by combining Processes A.1 and A.2.

Process A.3.

Input: Combinations l1,,lml_{1},\ldots,l_{m} and a pivot sequence (p1,,pj1)(p_{1},\ldots,p_{j-1}).

Output: Either 0 or a combination pj𝒦l1,,lm,p1,,pjp_{j}\in\mathcal{K}^{*}_{l_{1},\ldots,l_{m},p_{1},\ldots,p_{j}} such that smin(pj)=js_{\mathrm{min}}(p_{j})=j.

  1. 1.

    Receive (qi,𝐤i)(q_{i},{\bf k}_{i}) from Process A.1 for the inputs L={l1,,lm}L=\{l_{1},\ldots,l_{m}\} and (p1,,pj1)(p_{1},\ldots,p_{j-1}).

  2. 2.

    Return 0 if qi=0q_{i}=0.

  3. 3.

    Receive qjq_{j} from Process A.2 for the inputs (pi,,pj1)(p_{i},\ldots,p_{j-1}) and (qi,𝐤i)(q_{i},{\bf k}_{i}).

  4. 4.

    Return pj=qjp_{j}=q_{j}.

Process A.3 is essential for finding a pivot combination whose minimum variable is xjx_{j}, because we can find out it by Process A.3 if and only if it exists.

Proposition A.4.

For combinations l1,,lml_{1},\ldots,l_{m} and a pivot sequence (p1,,pj1)(p_{1},\ldots,p_{j-1}), the following statements are equivalent.
(i) Process A.3 outputs pj𝒦l1,,lm,p1,,pj1p_{j}\in\mathcal{K}^{*}_{l_{1},\ldots,l_{m},p_{1},\ldots,p_{j-1}} such that smin(pj)=js_{\mathrm{min}}(p_{j})=j.
(ii) There exists a combination pj𝒦l1,,lm,p1,,pj1p_{j}\in\mathcal{K}^{*}_{l_{1},\ldots,l_{m},p_{1},\ldots,p_{j-1}} such that smin(pj)=js_{\mathrm{min}}(p_{j})=j.


Proof.  Obviously, (i) implies (ii). Suppose (ii) is true to prove the converse. Then there exist elements c1,,cm,d1,,dj1c_{1},\ldots,c_{m},d_{1},\ldots,d_{j-1} in KK such that

pj\displaystyle p_{j} =\displaystyle= hchlh+idipi.\displaystyle\sum_{h}c_{h}l_{h}+\sum_{i}d_{i}p_{i}.

We have pj|(xj,xj+1,,xn)=(1,0,,0)Kp_{j}{\,|\,}_{(x_{j},x_{j+1},\ldots,x_{n})=(1,0,\ldots,0)}\in K^{*} since xjx_{j} is the minimum variable in pjp_{j}.

We first consider the situation where we run Process A.1 for the inputs L={l1,,lm}L=\{l_{1},\ldots,l_{m}\} and (p1,,pj1)(p_{1},\ldots,p_{j-1}): however, we temporally assume that step 3 is skipped and the process ends with the output (0,)(0,\varnothing) at step 4 of i=1i=1. Let k1,,kj1k_{1},\ldots,k_{j-1} be the elements in KK which are recursively determined as at step 55, and let 𝐤=(k1,,kj1,1,0,,0)Kn{\bf k}=(k_{1},\ldots,k_{j-1},1,0,\ldots,0)\in K^{n}. Then p1(𝐤)==pj1(𝐤)=0p_{1}({\bf k})=\cdots=p_{j-1}({\bf k})=0, and

pj(𝐤)\displaystyle p_{j}({\bf k}) =\displaystyle= hchlh(𝐤)+idipi(𝐤)=hchlh(𝐤).\displaystyle\sum_{h}c_{h}l_{h}({\bf k})+\sum_{i}d_{i}p_{i}({\bf k})\,=\,\sum_{h}c_{h}l_{h}({\bf k}).

Since pj(𝐤)=pj|(xj,xj+1,,xn)=(1,0,,0)Kp_{j}({\bf k})=p_{j}{\,|\,}_{(x_{j},x_{j+1},\ldots,x_{n})=(1,0,\ldots,0)}\in K^{*}, this implies lh(𝐤)Kl_{h}({\bf k})\in K^{*} for some hh, which means that Process A.1 can find out qiq_{i} in step 2 such that qi|(xi,,xn)=𝐤iKq_{i}{\,|\,}_{(x_{i},\ldots,x_{n})={\bf k}_{i}}\in K^{*}, at least when i=smin(lh)i=s_{\mathrm{min}}(l_{h}). Therefore, Process A.1 without the temporal assumption always outputs (qi,𝐤i)(0,)(q_{i},{\bf k}_{i})\neq(0,\varnothing).

We input L={l1,,lm}L=\{l_{1},\ldots,l_{m}\} and (p1,,pj1)(p_{1},\ldots,p_{j-1}) into Process A.3. At step 11, we receive (qi,𝐤i)(0,)(q_{i},{\bf k}_{i})\neq(0,\varnothing) from Process A.1. Thus step 2 is skipped, and qjq_{j} is received from Process A.2 at step 3, which satisfies the condition required in (i). Since pj=qjp_{j}=q_{j} is returned at step 4, we conclude (i) holds. \Box

For a subscript jj and a pivot sequence (pig)=(pig)1gh(p_{i_{g}})=(p_{i_{g}})_{1\leq g\leq h} with ih<ji_{h}<j, we define

D(pig),j\displaystyle D_{(p_{i_{g}}),j} :=\displaystyle:= [j1]{i1,,ih}.\displaystyle[j-1]\setminus\{i_{1},\ldots,i_{h}\}.

We call an integer in D(pig),jD_{(p_{i_{g}}),j} a deficient subscript, and a variable xix_{i} with iD(pig),ji\in D_{(p_{i_{g}}),j} a deficient variable.

We need to modify Process A.3 for practical use.

Process A.5.

Input: Combinations l1,,lml_{1},\ldots,l_{m}, a subscript jj, and a pivot sequence (pig)1gh(p_{i_{g}})_{1\leq g\leq h} with ih<ji_{h}<j.

Output: Either 0 or a combination pj𝒦l1,,lm,pi1,,pihp_{j}\in\mathcal{K}^{*}_{l_{1},\ldots,l_{m},p_{i_{1}},\ldots,p_{i_{h}}} with smin(pj)D(pig),j{j}s_{\mathrm{min}}(p_{j})\in D_{(p_{i_{g}}),j}\cup\{j\}.

  1. 1.

    Change the variable order by moving the deficient variables backward.

  2. 2.

    Prepare the pivot sequence (p1,,pj1|D(pig),j|)(p^{\prime}_{1},\ldots,p^{\prime}_{j-1-|D_{(p_{i_{g}}),j}|}) for the new variable order.

  3. 3.

    Receive pj|D(pig),j|p^{\prime}_{j-|D_{(p_{i_{g}}),j}|} from Process A.3 for the inputs l1,,lml_{1},\ldots,l_{m} and (p1,,pj1|D(pig),j|)(p^{\prime}_{1},\ldots,p^{\prime}_{j-1-|D_{(p_{i_{g}}),j}|}).

  4. 4.

    Undo the variable order by putting the deficient variables back to their original places.

  5. 5.

    Return pj=pj|D(pig),j|p_{j}=p^{\prime}_{j-|D_{(p_{i_{g}}),j}|}.

We are in a position to state Algorithm A.6 for a fast Gaussian forward elimination.

Algorithm A.6.

Input: Combinations L={l1,,lm}L=\{l_{1},\ldots,l_{m}\}.

Output: A pivot sequence (pig)(p_{i_{g}}).

  1. 1.

    Create subsets Li={lL|smin(l)=i}L_{i}=\{l\in L{\,|\,}s_{\mathrm{min}}(l)=i\} (i=1,,n)(i=1,\ldots,n).

  2. 2.

    Set j=0j=0 and (pig)=ϕ(p_{i_{g}})=\phi.

  3. 3.

    Execute the following loop process to make a pivot sequence (pig)(p_{i_{g}}):

    1. (i)

      Update jj+1j\leftarrow j+1 if j<nj<n; otherwise break.

    2. (ii)

      If LjϕL_{j}\neq\phi, append a combination in LiL_{i} to (pig)(p_{i_{g}}) and go back to (i).

    3. (iii)

      Receive pjp_{j} from Process A.5 for the inputs L1Lj1L_{1}\cup\cdots\cup L_{j-1} and (pig)(p_{i_{g}}).

    4. (iv)

      If pj=0p_{j}=0, go back to (i).

    5. (v)

      Append pjp_{j} to (pig)(p_{i_{g}}),999 The loop process ensures smin(pj)=js_{\mathrm{min}}(p_{j})=j. To show this, we may prove smin(pj)D(pig),js_{\mathrm{min}}(p_{j})\notin D_{(p_{i_{g}}),j} by the output condition in Process A.5. Suppose smin(pj)D(pig),js_{\mathrm{min}}(p_{j})\in D_{(p_{i_{g}}),j} and set j=smin(pj)<jj^{\prime}=s_{\mathrm{min}}(p_{j})<j. Then, on the jj^{\prime}-round in the loop process, Process A.5 at (iii) must return a non-zero combination by Proposition A.4 and the existence of pjp_{j}, where note that Process A.5 is essentially Process A.3. This means a combination pjp_{j^{\prime}} satisfying smin(pj)=js_{\mathrm{min}}(p_{j^{\prime}})=j^{\prime} must be appended to (pig)(p_{i_{g}}) at (v) on the jj^{\prime}-round, which contradicts j=smin(pj)D(pig),jj^{\prime}=s_{\mathrm{min}}(p_{j})\in D_{(p_{i_{g}}),j}. and back to (i).

  4. 4.

    Return (pig)(p_{i_{g}}).

The pivot sequence (pig)(p_{i_{g}}) outputted by Algorithm A.6 is a row echelon matrix under the order x1<<xnx_{1}<\cdots<x_{n} thanks to Proposition A.4 (see the footnote in (v) of step 3 for details).

Remark A.7.

Process A.1 is influenced by the unit propagation (UP) in the algorithm to solve the Boolean satisfiability (SAT) problem (see, e.g., [4, Chapter 1]). SAT is the first problem that was proved to be NP-complete, which means that all NP-problems are at most as difficult as SAT. UP is a technique to determine an assignment value for the variable we watch while searching a conflict combination (or a conflict clause in SAT terminology).

Process A.2 is inspired by the conflict-driven clause learning (CDCL) proposed in [3, 25, 26] (see also [4, Chapter 5]). CDCL enable us to find (or learn) a new pivot combination from the conflict evidence found by UP.

The performance of UP tends to increase when combinations have good structures for finding conflict combinations under a good variable order: i.e., not too few number of combinations, high frequency of small size combinations, bias of occurrences of variables, and so on. We have seen in Table 7 that the runtimes of MJPO\mathrm{MJPO} and EDS are much better than those of KNT\mathrm{KNT}, which seems to be due to the difference in numbers of relations (or combinations).

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