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The L'vov–Kaplansky conjecture for polynomials of degree four

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Abstract

We prove the L'vov–Kaplansky conjecture for multilinear polynomials of degree four over algebraically closed fields of characteristic zero.

Introduction

The commutator problem is an early instance of the problem of describing polynomial images on matrix algebras. For matrices AA, BB over a field, the additive commutator [A,B]=AB−BA[A,B]=AB-BA has trace zero, since tr⁡(AB)=tr⁡(BA)\operatorname{tr}(AB)=\operatorname{tr}(BA). Shoda proved the converse in characteristic zero: every matrix of trace zero is one commutator [1 Satz 3]. Albert and Muckenhoupt extended the result to arbitrary fields [2]. Thus a linear condition on matrix entries completely describes the image of this particular noncommutative polynomial. Replacing the commutator by a general polynomial leads to the image problem considered here.

Throughout this paper KK is an algebraically closed field of characteristic zero. Let Mn(K)M_n(K) be the algebra of n×nn\times n matrices, InI_n its identity matrix, and tr⁡T=∑i=1nTii\operatorname{tr} T=\sum_{i=1}^{n}T_{ii} the ordinary, unnormalized trace. Write

sln(K)={T∈Mn(K):tr⁡T=0}.\mathfrak{sl}_n(K)=\{T\in M_n(K):\operatorname{tr} T=0\}.

The free associative algebra K⟨X1,…,Xm⟩K\langle X_1,\ldots,X_m\rangle has the words in its noncommuting generators as a KK-basis, with multiplication by concatenation. An ordinary multilinear polynomial of degree mm is

p(X1,…,Xm)=∑σ∈SmaσXσ(1)⋯Xσ(m),aσ∈K.p(X_1,\ldots,X_m)=\sum_{\sigma\in S_m}a_\sigma X_{\sigma(1)}\cdots X_{\sigma(m)},\qquad a_\sigma\in K.

where SmS_m is the permutation group of {1,…,m}\{1,\ldots,m\}. The integer mm counts inputs and the integer nn is the matrix order. The image is the set

p(Mn(K))={p(A1,…,Am):Ai∈Mn(K)}.p(M_n(K))=\{p(A_1,\ldots,A_m):A_i\in M_n(K)\}.

A polynomial identity has zero image. A central polynomial has nonzero image consisting of scalar matrices. These two possibilities arise naturally in polynomial identity theory: the standard identity of Amitsur and Levitzki [3] and the central polynomials of Formanek [4] give examples.

The L’vov–Kaplansky question asks whether p(Mn(K))p(M_n(K)) is always a vector space. It is recorded as Question 1.98 of the fourth edition of the Dniester Notebook [7 Introduction]; Kanel-Belov, Malev, and Rowen discuss its attribution to Kaplansky and its formulation by

2020 Mathematics Subject Classification. Primary 16R10; Secondary 15A24, 16R30.

Keywords. Multilinear polynomial, matrix algebra, L’vov–Kaplansky conjecture. L’vov [10 Introduction]. Over the present field, an affirmative answer says that the image is one of

{0},KIn,sln(K),Mn(K).\{0\},\quad K I_n,\quad\mathfrak{sl}_n(K),\quad M_n(K).

Each of these possibilities occurs among multilinear polynomials. The zero polynomial and the identity input give the first and fourth, the commutator gives the third, and multilinear central polynomials give the second.

The linear span of the image has a simpler description than the image itself. Brešar and Klep proved that this span is a Lie ideal and classified it by the polynomial’s identities and trace [5 Theorems 2.3 and 4.5]. For a multilinear polynomial, the underlying identity is

[p(A1,…,Am),T]=∑i=1mp(A1,…,[Ai,T],…,Am).[p(A_1,\ldots,A_m),T]=\sum_{i=1}^{m}p(A_1,\ldots,[A_i,T],\ldots,A_m).

Their tracial Nullstellensatz identifies polynomials of zero trace with sums of commutators modulo matrix identities. The image problem asks which matrices occur as single values. In particular, passing from traceless containment to full image requires attaining each conjugacy class of nonzero trace.

There are two useful ways to restrict the question: fix the matrix order and allow arbitrary polynomial degree, or fix the degree and allow arbitrary matrix order. Kanel-Belov, Malev, and Rowen proved the full image classification in matrix order two [10 Theorem 2]. Their work combines conjugation-invariant cones, generic matrices, and invariant theory. In matrix order three, their subsequent paper obtains image and density restrictions, with possible power-central behavior [8].

Mesyan generalized the commutator theorem by proving that every nonzero multilinear polynomial of degree at most three contains all traceless matrices in its image, in the range of field sizes stated in [6 Theorem 13]. He proposed the corresponding containment for a polynomial of degree mm when n≥m−1n \ge m-1. This weaker problem is usually called the Mesyan conjecture. Buzinski and Winstanley treated degree four for algebraically closed fields of characteristic zero and n≥3n \ge3 [7]. A lemma in that argument required correction. Fagundes, de Mello, and dos Santos supplied the correction and established

sln(K)⊆p(Mn(K))(n≥3)(1)\mathfrak{sl}_n(K) \subseteq p(M_n(K)) \qquad(n \ge3) \tag*{(1)}

for every nonzero multilinear quartic [13 Theorem 8]. Thus the trace-zero quartic case follows from existing work. The trace-nonzero case requires attainment of the remaining conjugacy classes.

For the full conjecture in degree three, Dykema and Klep proved the result for every even matrix order and every odd order below 17 [11 Theorem 1.2]. Their proof uses Sylvester’s clock-and-shift basis and determinant conditions on the six word coefficients. Vitas proved the degree-three conjecture in all orders [12 Theorem B], by constructing slices covering prescribed Jordan forms and solving the resulting coefficient conditions. We use his theorem when replacing one input of a quartic by the identity leaves a cubic whose trace is nonzero.

We prove the full degree-four conjecture. The additional conclusion beyond (1) is that every trace-nonzero quartic has image Mn(K)M_n(K) for n≥3n \ge3.

Theorem 1.1. Let pp be an ordinary multilinear polynomial of degree four over KK. For every n≥3n \ge3,

p(Mn(K))={{0},p=0,sln(K),p≠0 and tr⁡p is identically zero,Mn(K),tr⁡p is not identically zero.p(M_n(K))= \begin{cases} \{0\}, & p=0,\\ \mathfrak{sl}_n(K), & p\ne0\ \text{and}\ \operatorname{tr}p\ \text{is identically zero},\\ M_n(K), & \operatorname{tr}p\ \text{is not identically zero}. \end{cases}

Consequently the image of pp is a vector space in every positive matrix order.

Two words are cyclically equivalent when one is obtained by moving an initial segment of the other to its end. The formal trace of a polynomial is its class in the vector space with cyclic word classes as a basis. For a quartic the six class coefficients are the sums of the four word coefficients in each class. Matrix trace vanishes on the difference of cyclically equivalent words. Conversely, rotate each word to place X4X_4 last and let W(X1,X2,X3)W(X_1, X_2, X_3) be the resulting cubic. Then

tr⁡p(A1,A2,A3,A4)=tr⁡(W(A1,A2,A3)A4).\operatorname{tr} p(A_1,A_2,A_3,A_4)=\operatorname{tr}\bigl(W(A_1,A_2,A_3)A_4\bigr).

The trace pairing is the bilinear form (U,V)↦tr⁡(UV)=∑i,jUijVji(U,V) \mapsto\operatorname{tr}(UV)=\sum_{i,j}U_{ij}V_{ji}; it is nondegenerate because each entry of UU can be tested by a suitable VV. If the left side vanishes identically, this pairing makes WW an identity on Mn(K)M_n(K). The minimal nonzero identity degree is 2n2n [3], so W=0W=0 for n≥3n\geq3. This proves equivalence between formal and evaluated trace vanishing in Theorem 1.1.

We fix all but one input and study the image of the resulting linear map. Weighted permutation inputs produce slices containing subspaces defined by prescribed zero entries. Scalar shifts of identity reductions produce slices of rank at least n2−1n^2-1. The coefficients left by these constructions form a six-dimensional space. Two involution substitutions reduce it to eight families, and five explicit 3×33\times3 slices settle each family. A block construction passes from sizes two and three to every larger size. The coefficient matrices, minors, and factor-by-factor elimination calculations are given in the appendices.

Linear slices and conjugacy classes

For a chosen input position, a slice is the linear map obtained by fixing the other inputs. Its image is a subspace of Mn(K)M_n(K), and its rank is the dimension of that image. Slice rank conditions are Zariski open conditions on the frozen inputs, since they assert the nonvanishing of a minor. We repeatedly intersect finitely many nonempty open subsets of an affine space. Such an intersection is nonempty because its coordinate ring is an integral domain. Matrix indices in the proofs below run from 1 to nn, unless explicitly stated otherwise. The matrix unit EijE_{ij} has its only nonzero entry, equal to one, in position (i,j)(i,j).

A conjugacy class of TT is {gTg−1:g∈GL⁡n(K)}\{gTg^{-1}:g\in\operatorname{GL}_n(K)\}, where GL⁡n(K)\operatorname{GL}_n(K) is the group of invertible matrices; we write C∼TC\sim T when CC belongs to this class. For a subspace HH, its orthogonal space for the trace pairing is H⊥={A:tr⁡(AX)=0 for every X∈H}H^\perp=\{A:\operatorname{tr}(AX)=0\text{ for every }X\in H\}. The codimension of HH is the dimension of its ambient space minus dim⁡H\dim H.

Lemma 2.1. For n≥2n\geq2, every nonzero conjugation-invariant subspace of sln(K)\mathfrak{sl}_n(K) is sln(K)\mathfrak{sl}_n(K). The span of the conjugates of a nonscalar matrix contains sln(K)\mathfrak{sl}_n(K). The differences of matrices in any nonscalar conjugacy class span sln(K)\mathfrak{sl}_n(K). Rank-one square-zero matrices also span sln(K)\mathfrak{sl}_n(K).

Proof. For the first assertion, diagonal conjugation has one-dimensional weight spaces KEijK E_{ij}, i≠ji\ne j, and its weight-zero space is diagonal. If an invariant subspace contains a matrix with a nonzero off-diagonal entry, extraction of its character component gives EijE_{ij} in that subspace. This extraction is elementary: distinct Laurent monomials on (K×)n(K^\times)^n are linearly independent, and evaluation at finitely many diagonal matrices recovers their coefficient vectors by linear algebra. Permutation conjugation gives every off-diagonal matrix unit. Conjugating EijE_{ij} by In+tEjiI_n+tE_{ji} gives

Eij+t(Ejj−Eii)−t2Eji,E_{ij}+t(E_{jj}-E_{ii})-t^2E_{ji},

so the diagonal differences belong as well. If the initial nonzero matrix is diagonal, it is nonscalar because its trace is zero; conjugation by In+tEijI_n+tE_{ij} for unequal diagonal entries yields a nonzero off-diagonal component. This proves the first assertion.

The span of the conjugates of a nonscalar matrix is invariant. The linear span of differences of conjugates is a nonzero invariant subspace of sln(K)\mathfrak{sl}_n(K), since a matrix fixed by every similarity is scalar. Applying the first assertion proves the next two claims. Finally let eie_i be the iith standard column and ei∗e_i^* its coordinate functional. The matrix EijE_{ij} is rank one and square zero for i≠ji \ne j, and

Eii−Ejj=(ei+ej)(ei∗−ej∗)+Eij−Eji.E_{ii}-E_{jj}=(e_i+e_j)(e_i^*-e_j^*)+E_{ij}-E_{ji}.

The outer product in this expression is rank one and square zero, because (ei∗−ej∗)(ei+ej)=0(e_i^*-e_j^*)(e_i+e_j)=0. These matrices span sln(K)\mathfrak{sl}_n(K).

Lemma 2.2. If an ordinary multilinear polynomial has a value of nonzero trace on Mn(K)M_n(K), its image contains every scalar matrix.

Proof. Choose a primitive nnth root of unity ζ∈K\zeta\in K. Define DD to be diagonal with entries 1,ζ,…,ζn−11,\zeta,\ldots,\zeta^{n-1}, and define the cyclic shift RR by Rei=ei+1Re_i=e_{i+1} with cyclic indices. They satisfy DR=ζRDDR=\zeta RD, including at the cyclic boundary, and therefore

(DaRb)(DcRd)=ζ−bcDa+cRb+d.(D^aR^b)(D^cR^d)=\zeta^{-bc}D^{a+c}R^{b+d}.

Exponents are taken modulo nn. For each fixed bb, these matrices occupy one cyclic diagonal and the coefficient matrix in aa is the invertible Vandermonde matrix (ζai)a,i=0n−1(\zeta^{ai})_{a,i=0}^{n-1}. Different bb give disjoint diagonals. Hence the n2n^2 matrices form a basis. Their trace is zero for b≠0b \ne0; when b=0b=0 it is ∑i=0n−1ζai\sum_{i=0}^{n-1}\zeta^{ai}, equal to zero unless a=0a=0, in which case it is n≠0n \ne0. Expand the inputs of a value of nonzero trace in this basis. Some basis-input evaluation has nonzero trace. All its words have the same pair of total indices, so that evaluation is a nonzero scalar matrix. Scaling one input gives the assertion.

References

  1. [1]K. Shoda, Einige Sätze über Matrizen, Japanese J. Math. 13 (1936), 361–365, doi:10.4099/jjm1924.13.0_361.DOI
  2. [2]A. A. Albert and B. Muckenhoupt, On matrices of trace zeros, Michigan Math. J. 4 (1957), 1–3.DOI
  3. [3]S. A. Amitsur and J. Levitzki, Minimal identities for algebras, Proc. Amer. Math. Soc. 1 (1950), 449–463.DOI
  4. [4]E. Formanek, Central polynomials for matrix rings, J. Algebra 23 (1972), 129–132.DOI
  5. [5]M. Brešar and I. Klep, Values of noncommutative polynomials. Lie skew-ideals und tracial Nullstellensätze, Math. Res. Lett. 16 (2009), 605–626; arXiv:0810.1774.
  6. [6]Z. Mesyan, Polynomials of small degree evaluated on matrices, Linear Multilinear Algebra 61 (2013), 1487–1495; arXiv:1212.1925.DOI
  7. [7]D. Buzinski and R. Winstanley, On multilinear polynomials in four variables evaluated on matrices, Linear Algebra Appl. 439 (2013), 2712–2719; arXiv:1511.06331.
  8. [8]A. Kanel-Belov, S. Malev, and L. Rowen, The images of multilinear polynomials evaluated on 3 × 3 matrices, Proc. Amer. Math. Soc. 144 (2016), 7–19; arXiv:1306.4389v3.
  9. [9]A. Kanel-Belov, S. Malev, L. Rowen, and R. Yavich, Evaluations of noncommutative polynomials on algebras: methods and problems, and the L’vov–Kaplansky conjecture, arXiv:1909.07785v4.
  10. [10]A. Kanel-Belov, S. Malev, and L. Rowen, The images of non-commutative polynomials evaluated on 2 × 2 matrices, Proc. Amer. Math. Soc. 140 (2012), 465–478.
  11. [11]K. Dykema and I. Klep, Instances of the Kaplansky–Lvov multilinear conjecture for polynomials of degree three, Linear Algebra Appl. 508 (2016), 272–288.DOI
  12. [12]D. Vitas, The L’vov–Kaplansky conjecture for polynomials of degree three, Linear Algebra Appl. 733 (2026), 205–232, doi:10.1016/j.laa.2025.12.016; arXiv:2310.15600v2.DOI
  13. [13]P. S. Fagundes, T. C. de Mello, and P. H. da Silva dos Santos, The Mesyan conjecture: a restatement and a correction, arXiv:2111.13698v1.DOI
  14. [14]M. de Bondt, Mathieu subspaces of codimension less than n of Mat_n(K), arXiv:1310.7843v2.
  15. [15]P. A. Fillmore, On similarity and the diagonal of a matrix, Amer. Math. Monthly 76 (1969), 167–169.DOI

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