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Electronic Colloquium on Computational Complexity

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Reports tagged with Algebraic Branching Programs:
TR09-122 | 23rd November 2009
Vikraman Arvind, Srikanth Srinivasan

Circuit Lower Bounds, Help Functions, and the Remote Point Problem

We investigate the power of Algebraic Branching Programs (ABPs) augmented with help polynomials, and constant-depth Boolean circuits augmented with help functions. We relate the problem of proving explicit lower bounds in both these models to the Remote Point Problem (introduced by Alon, Panigrahy, and Yekhanin (RANDOM '09)). More precisely, proving ... more >>>

TR10-015 | 8th February 2010
Maurice Jansen, Youming Qiao, Jayalal Sarma

Deterministic Black-Box Identity Testing $\pi$-Ordered Algebraic Branching Programs

In this paper we study algebraic branching programs (ABPs) with restrictions on the order and the number of reads of variables in the program. Given a permutation $\pi$ of $n$ variables, for a $\pi$-ordered ABP ($\pi$-OABP), for any directed path $p$ from source to sink, a variable can appear at ... more >>>

TR10-084 | 14th May 2010
Maurice Jansen, Youming Qiao, Jayalal Sarma

Deterministic Identity Testing of Read-Once Algebraic Branching Programs

An algebraic branching program (ABP) is given by a directed acyclic graph with source and sink vertices $s$ and $t$, respectively, and where edges are labeled by variables or field constants. An ABP computes the sum of weights of all directed paths from $s$ to $t$, where the weight of ... more >>>

TR10-118 | 27th July 2010
Maurice Jansen

Extracting Roots of Arithmetic Circuits by Adapting Numerical Methods

Revisions: 2

For two polynomials $f \in \mathbb{F}[x_1, x_2, \ldots, x_n, y]$ and $p \in \mathbb{F}[x_1, x_2, \ldots, x_n]$, we say that $p$ is a root of $f$, if $f(x_1, x_2, \ldots, x_n, p) \equiv 0$. We study the relation between the arithmetic circuit sizes of $f$ and $p$ for general circuits ... more >>>

TR11-083 | 22nd May 2011
Eric Allender, Fengming Wang

On the power of algebraic branching programs of width two

We show that there are families of polynomials having small depth-two arithmetic circuits that cannot be expressed by algebraic branching programs of width two. This clarifies the complexity of the problem of computing the product of a sequence of two-by-two matrices, which arises in several

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TR17-028 | 17th February 2017
Mrinal Kumar

A quadratic lower bound for homogeneous algebraic branching programs

Revisions: 1

An algebraic branching program (ABP) is a directed acyclic graph, with a start vertex $s$, and end vertex $t$ and each edge having a weight which is an affine form in $\F[x_1, x_2, \ldots, x_n]$. An ABP computes a polynomial in a natural way, as the sum of weights of ... more >>>

TR18-029 | 9th February 2018
Neeraj Kayal, vineet nair, Chandan Saha

Average-case linear matrix factorization and reconstruction of low width Algebraic Branching Programs

Revisions: 2

Let us call a matrix $X$ as a linear matrix if its entries are affine forms, i.e. degree one polynomials. What is a minimal-sized representation of a given matrix $F$ as a product of linear matrices? Finding such a minimal representation is closely related to finding an optimal way to ... more >>>

TR18-038 | 21st February 2018
Nathanael Fijalkow, Guillaume Lagarde, Pierre Ohlmann

Tight Bounds using Hankel Matrix for Arithmetic Circuits with Unique Parse Trees

This paper studies lower bounds for arithmetic circuits computing (non-commutative) polynomials. Our conceptual contribution is an exact correspondence between circuits and weighted automata: algebraic branching programs are captured by weighted automata over words, and circuits with unique parse trees by weighted automata over trees.

The key notion for understanding the ... more >>>

TR19-170 | 27th November 2019
Prerona Chatterjee, Mrinal Kumar, Adrian She, Ben Lee Volk

A Quadratic Lower Bound for Algebraic Branching Programs

Revisions: 3

We show that any Algebraic Branching Program (ABP) computing the polynomial $\sum_{i = 1}^n x_i^n$ has at least $\Omega(n^2)$ vertices. This improves upon the lower bound of $\Omega(n\log n)$, which follows from the classical result of Baur and Strassen [Str73, BS83], and extends the results by Kumar [Kum19], which showed ... more >>>

TR20-031 | 10th March 2020
Markus Bläser, Christian Ikenmeyer, Meena Mahajan, Anurag Pandey, Nitin Saurabh

Algebraic Branching Programs, Border Complexity, and Tangent Spaces

Nisan showed in 1991 that the width of a smallest noncommutative single-(source,sink) algebraic branching program (ABP) to compute a noncommutative polynomial is given by the ranks of specific matrices. This means that the set of noncommutative polynomials with ABP width complexity at most $k$ is Zariski-closed, an important property in ... more >>>

TR20-077 | 19th May 2020
Amit Sinhababu, Thomas Thierauf

Factorization of Polynomials Given by Arithmetic Branching Programs

Given a multivariate polynomial computed by an arithmetic branching program (ABP) of size $s$, we show that all its factors can be computed by arithmetic branching programs of size $\text{poly}(s)$. Kaltofen gave a similar result for polynomials computed by arithmetic circuits. The previously known best upper bound for ABP-factors was ... more >>>

TR20-129 | 5th September 2020
Mrinal Kumar, Ben Lee Volk

A Lower Bound on Determinantal Complexity

The determinantal complexity of a polynomial $P \in \mathbb{F}[x_1, \ldots, x_n]$ over a field $\mathbb{F}$ is the dimension of the smallest matrix $M$ whose entries are affine functions in $\mathbb{F}[x_1, \ldots, x_n]$ such that $P = Det(M)$. We prove that the determinantal complexity of the polynomial $\sum_{i = 1}^n x_i^n$ ... more >>>

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