arXiv subjects
Publications and source records attributed to Igor S. Sergeev.
It is shown that the $\mathsf{OR}$-rank (covering rank) of the $2^n \times 2^n$ unique disjointness matrix is $n^{O(1)}(3/2)^n$, hence the known lower bound $1.5^n$ turns out to be essentially tight. By the way, an upper bound $1.89^n$ is obtained for the $\mathsf{SUM}$-rank (partition rank) of this matrix.
This article provides a survey of circuit complexity bounds for basic boolean transforms exploited in digital circuit design and efficient methods for synthesizing such circuits. The exposition covers structurally simple functions and operators, such as counters, adders, encoders, and multiplexors, and excludes more complex algebraic operations with numbers, polynomials, and matrices. Several applications to implementing more specific operations are also discussed.
It is shown that the minimal depth of an optimal prefix circuit (i.e., a zero-deficiency circuit) on $N$ inputs with fanout bounded by $k$ is ${\log_{α_k} N \pm O(1)}$, where $α_k$ is the unique positive root of the polynomial ${2+x+ x^2+\ldots + x^{k-2}-x^k}$. This bound was previously known in the cases $k=2$ and $k=\infty$.
For any $n$ and $k$, we provide an explicit (that is, computable in polynomial time) example of integer $B_k$-sequence of size $n$ consisting of elements bounded by $n^{k+o(k)}$.
In the present note, we study a new method of constructing efficient coverings for Kronecker powers of matrices, recently proposed by J. Alman, Y. Guan, A. Padaki [arXiv, 2022]. We provide an alternative proof for the case of symmetric matrices in a stronger form. As a consequence, the previously known upper bound on the depth-2 additive complexity of the boolean $N\times N$ Kneser-Sierpinski matrices is improved to $O(N^{1.251})$.
We show that the complexity of minimal monotone circuits implementing a monotone version of the permutation operator on $n$ boolean vectors of length $q$ is $Θ(qn\log n)$. In particular, we obtain an alternative way to prove the known complexity bound $Θ(n\log n)$ for the monotone shift operator on $n$ boolean inputs.
We propose an explanation of the structure of the best known sorting networks for 16 elements with respect to the complexity and to the depth due to Green and van Voorhis.
We prove that the complexity of computing the table of primes between $1$ and $n$ on a multitape Turing machine is $O(n \log n)$.
We provide accurate upper bounds on the Boolean circuit complexity of the standard and the Karatsuba methods of integer multiplication
We construct explicit Boolean square matrices whose rectifier complexity (OR-complexity) differs significantly from the complexity of their complement matrices.
In the present note we show that for any positive integer k an arbitrary Boolean circulant matrix can be implemented via modulo 2 rectifier circuit of depth 2k-1 and complexity O(n^{1+1/k}), and also via circuit of depth 2k and complexity O(n^{1+1/k} log^{-1/k} n).
In the present note we prove an asymptotically tight relation between additive and multiplicative complexity of Boolean functions with respect to implementation by circuits over the basis {+,*,1}.
It is shown that the counting function of n Boolean variables can be implemented with the formulae of size O(n^3.06) over the basis of all 2-input Boolean functions and of size O(n^4.54) over the standard basis. The same bounds follow for the complexity of any threshold symmetric function of n variables and particularly for the majority function. Any bit of the product of binary numbers of length n can be computed by formulae of size O(n^4.06) or O(n^5.54) depending on basis. Incidentally the bounds O(n^3.23) and O(n^4.82) on the formula size of any symmetric function of n variables with respect to the basis are obtained.
It is shown that the exponential of a complex power series up to order n can be implemented via (23/12+o(1))M(n) binary arithmetic operations over complex field, where M(n) stands for the (smoothed) complexity of multiplication of polynomials of degree <n in FFT-model. Yet, it is shown how to raise a power series to a constant power with the complexity (27/8+o(1))M(n).