30

There's an algorithm for multiplying an $N \times N^{0.172}$ matrix with an $N^{0.172} \times N$ matrix in $N^2 \operatorname{polylog}\left(N\right)$ arithmetic operations. The main identity used for it comes from Coppersmith's paper "Rapid multiplication of rectangular matrices", but the explanation for why it leads to $N^2 \operatorname{polylog}\left(N\...


20

The decision versions of many common problems in linear algebra over the integers (or rationals) are in the class $\mathsf{DET}$, see the paper Gerhard Buntrock, Carsten Damm, Ulrich Hertrampf, Christoph Meinel: Structure and Importance of Logspace-MOD Class. Mathematical Systems Theory 25(3): 223-237 (1992) $\mathsf{DET}$ is contained in $\mathsf{DSPACE}(...


18

It is equivalent to deciding whether two given multigraphs (or edge-labelled graphs) are isomorphic or not, which is known to be equivalent to the usual graph isomorphism problem.


17

Your question is equivalent to whether $A_1, \dotsc, A_k$ generate a nilpotent algebra, which in turn is equivalent to each of the $A_i$ being nilpotent. Hence not only is it decidable, but in $\tilde O(n^{2 \omega})$ time where $\omega$ is the exponent of matrix multiplication. Let $\mathcal{A}$ be the associative algebra generated by the $A_i$: that is, ...


16

The space usage is at most $O(n^2)$ for all Strassen-like algorithms (i.e. those based on upper bounding the rank of matrix multiplication algebraically). See Space complexity of Coppersmith–Winograd algorithm However, I realized in my previous answer that I did not explain why the space usage is $O(n^2)$... so here goes something hand-wavy. Consider what a ...


15

De Groote (On Varieties of Optimal Algorithms for the Computation of Bilinear Mappings. II. Optimal Algorithms for 2x2-Matrix Multiplication. Theor. Comput. Sci. 7: 127-148, 1978) proves that there is only one algorithm to multiply $2 \times 2$-matrices with 7 multiplications up to equivalence. This might be a unique feature of $2 \times 2$-matrix ...


14

The conjecture fails over $\mathbb{F}_2$. Look at $M(x, y) = \langle x, y \rangle \bmod 2$, and $x, y \in \{0, 1\}^n$. The communication complexity is $\Omega(n)$, but the rank of $M$ over $\mathbb{F}_2$ is $n$, by the linearity of inner product.


14

You ask why database aggregations have monoidal structure. Say we want to combine data values $a$ and $b$, but want to keep things general -- these may be integers, strings, floating point numbers, vectors, matrices, probability distributions, sets, or anything else we want to store and manipulate. So we denote the "aggregation" of $a$ and $b$ by $a.b$. ...


14

I think the answer to your first question is also $\widetilde O(n^3 \log( \|A\| + \|b\|))$ due to the following arguments: Edmonds' paper does not describe a variant of Gaussian elimination but it proves that any number computed in a step of the algorithm is a determinant of some submatrix of A. By Schrijver's book on Theory of Linear and Integer Programming ...


13

Classical work of Coppersmith shows that for some $\alpha > 0$, one can multiply an $n \times n^\alpha$ matrix with an $n^\alpha \times n$ matrix in $\tilde{O}(n^2)$ arithmetic operations. This is a crucial ingredient of Ryan Williams's recent celebrated result. François le Gall recently improved on Coppersmith's work, and his paper has just been ...


12

The exponent of computing a basis of the kernel is the same as the exponent of matrix multiplication, see the book Algebraic Complexity Theory by Bürgisser, Clausen & Shokrollahi. So it can be done in time $O(n^{2.38})$.


12

For matrices of sizes $k = 2,3$ the Matrix Powering Positivity Problem is in $\mathsf{P}$ (cf. this paper to appear in STACS 2015)


12

Isn't the following a counter-example? Let $f(x)$ be the majority of $x_1, \ldots, x_{1/\epsilon^2}$, which is an indicator of a set of size $2^n/2$, so $d = 1$. However, $\hat{f}(\{i\}) = \Theta(\epsilon)$ for $1 \le i \le 1/\epsilon^2$, so you have $1/\epsilon^2$ linearly independent large Fourier coefficients.


12

I cannot say for sure if you will consider the following approach as better, but from a complexity-theoretic point of view there is a more efficient solution. The idea is to rephrase your question in the first-order theory of the rationals with addition and order. You have that $P_1$ is included in $P_2$ if and only if \begin{align*} \Phi := \forall \vec{x}....


12

Consider the problem $\text{MAX-LIN}(R)$ of maximizing the number of satisfied linear equations over some ring $R$, which is often NP-hard, for example in the case $R=\mathbb{Z}$ Take an instance of this problem, $Ax=b$ where $A$ is a $n\times m$ matrix. Let $k=m+1$. Construct a new linear system $\tilde{A}\tilde{x} = \tilde{b}$, where $\tilde{A}$ is a $kn ...


12

There is a very neat randomized algorithm by Cheung, Kwok, and Lau, which computes the rank of an $n\times m$ matrix $A$, $n\le m$, in time $O(\mathrm{nnz}(A) + \min\{n^\omega, n\cdot\mathrm{nnz}(A)\})$. Here $\mathrm{nnz}(A)$ is the number of nonzero elements of $A$, and $\omega$ is the matrix multiplication exponent. Not quite what you are asking for, but ...


12

Take a look at https://www.sciencedirect.com/science/article/pii/S0022000008001141 "The complexity of satisfiability problems: Refining Schaefer's theorem" by Allender et al. which answer your questions: (1) Open (2) $\oplus L = L$ (3) Yes


12

Well, one thing is I think that all the constructions we know of - and even the families of potential constructions that people have proposed (e.g., Cohn-Umans approaches, generalizations of Coppersmith-Winograd) - would "simply" produce a family of algorithms $A_\epsilon$ running in time $O(n^{2+\epsilon})$. So to have a single algorithm which ran in $O(n^2 ...


11

There is some sort of explanation in the book Algebraic Complexity Theory by Bürgisser, Clausen and Shokrollahi (p. 11-12). The idea is to start with two bases $A_0,A_1,A_2,A_3$ and $B_0,B_1,B_2,B_3$ of the space of $2\times 2$ real matrices which satisfy the following property: $A_iB_j \in \{0,A_0,A_1,A_2,A_3,B_0,B_1,B_2,B_3\}$. Furthermore, $A_0 = B_0$. To ...


11

Matrices A and B whose elements are in a field F are similar (in F) if and only if they have the same Frobenius normal form. According to a quick search, it seems that the Frobenius normal form of an n×n matrix can be computed with O(n3) field operations [Sto98], and that this can be improved to something comparable to the complexity of matrix ...


11

This is a paper that seems to improve upon the result. https://arxiv.org/pdf/1209.3995.pdf


11

Josh Alman showed some cool lower bound results of MM, which won CCC 2019 best student paper award! http://drops.dagstuhl.de/opus/volltexte/2019/10834/pdf/LIPIcs-CCC-2019-12.pdf


10

The fastest way to multiply dense matrices on a modern computer is to call BLAS.


10

The approach you're describing is the generating function approach. By solving systems of polynomial equations, one can calculate the number of words of given length (generated by any non-terminal), and through it the corresponding asymptotics. For regular languages one can directly use linear algebra to estimate the number of words of given length. The ...


9

How can this system be solved without using linear programming? It cannot. Your problem is the linear feasibility problem in the standard form, with an extra condition that the constraint matrix is sparse. Now note: (1) It is well-known that linear programming is equivalent to the linear feasibility problem in the standard form. (2) You can easily ...


9

Perhaps you want what's sometimes called "Chang's Lemma" or "Talagrand's Lemma"... called the "Level-1 Inequality" here: http://analysisofbooleanfunctions.org/?p=885 It implies that if $1_S$ has mean $2^{-d}$ then the number of linearly independent Fourier coefficients whose square is at least $\gamma 2^{-d}$ is at most $O(d/\gamma^2)$. (This is because an ...


9

As others have said, there's no point in reinventing the wheel. If you must implement it yourself for whatever reason, then you should either go for the naïve or Strassen algorithm. Naïve is faster for smaller matrices, but as matrix size increases to ~100 you will find that the Strassen algorithm starts to perform better as the impact of the larger ...


9

It is known that the number of arithmetic operations necessary to compute the determinant of an $n\times n$ matrix is $n^{\omega+o(1)}$, where $\omega$ is the matrix multiplication constant. See for example this table on Wikipedia, as well as its footnotes and references. Note that the asymptotic complexity of matrix inversion is also the same as matrix ...


8

You can do this in uniform NC, see: G. Villard. Fast parallel algorithms for matrix reduction to canonical forms. AAECC 8:511-537, 1997. http://link.springer.com/article/10.1007%2Fs002000050089


8

No. Suppose all $a_i$'s are $0$ and all your $b_i$'s are equal; then the polytopes you can get by varying the $b_i$'s are essentially the hypersimplices. But the number of vertices of an $n$-dimensional hypersimplex can be any binomial coefficient $\binom{n}{k}$. In particular choosing $k=n/2$ gives an exponential number of extreme points.


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