arXiv subjects

Jayalal Sarma

Publications and source records attributed to Jayalal Sarma.

22 records · Page 2Linked to original sources

arXiv2016

Pebbling Meets Coloring: Reversible Pebble Game On Trees

The reversible pebble game is a combinatorial game played on rooted DAGs. This game was introduced by Bennett (1989) motivated by applications in designing space efficient reversible algorithms. Recently, Chan (2013) showed that the reversible pebble game number of any DAG is the same as its Dymond-Tompa pebble number and Raz-Mckenzie pebble number. We show, as our main result, that for any rooted directed tree T, its reversible pebble game number is always just one more than the edge rank coloring number of the underlying undirected tree U of T. It is known that given a DAG G as input, determining its reversible pebble game number is PSPACE-hard. Our result implies that the reversible pebble game number of trees can be computed in polynomial time. We also address the question of finding the number of steps required to optimally pebble various families of trees. It is known that trees can be pebbled in $n^{O(\log(n))}$ steps where $n$ is the number of nodes in the tree. Using the equivalence between reversible pebble game and the Dymond-Tompa pebble game (Chan, 2013), we show that complete binary trees can be pebbled in $n^{O(\log\log(n))}$ steps, a substantial improvement over the naive upper bound of $n^{O(\log(n))}$. It remains open whether complete binary trees can be pebbled in polynomial (in $n$) number of steps. Towards this end, we show that almost optimal (i.e., within a factor of $(1 + ε)$ for any constant $ε> 0$) pebblings of complete binary trees can be done in polynomial number of steps. We also show a time-space trade-off for reversible pebbling for families of bounded degree trees by a divide-and-conquer approach: for any constant $ε> 0$, such families can be pebbled using $O(n^ε)$ pebbles in $O(n)$ steps. This generalizes an analogous result of Kralovic (2001) for chains.

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arXiv2015

Depth Lower Bounds against Circuits with Sparse Orientation

We study depth lower bounds against non-monotone circuits, parametrized by a new measure of non-monotonicity: the orientation of a function $f$ is the characteristic vector of the minimum sized set of negated variables needed in any DeMorgan circuit computing $f$. We prove trade-off results between the depth and the weight/structure of the orientation vectors in any circuit $C$ computing the Clique function on an $n$ vertex graph. We prove that if $C$ is of depth $d$ and each gate computes a Boolean function with orientation of weight at most $w$ (in terms of the inputs to $C$), then $d \times w$ must be $Ω(n)$. In particular, if the weights are $o(\frac{n}{\log^k n})$, then $C$ must be of depth $ω(\log^k n)$. We prove a barrier for our general technique. However, using specific properties of the Clique function and the Karchmer-Wigderson framework (Karchmer and Wigderson, 1988), we go beyond the limitations and obtain lower bounds when the weight restrictions are less stringent. We then study the depth lower bounds when the structure of the orientation vector is restricted. Asymptotic improvements to our results (in the restricted setting), separates NP from NC. As our main tool, we generalize Karchmer-Wigderson gamefor monotone functions to work for non-monotone circuits parametrized by the weight/structure of the orientation. We also prove structural results about orientation and prove connections between number of negations and weight of orientations required to compute a function.

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arXiv2010

Deterministic Black-Box Identity Testing $π$-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 $π$ of $n$ variables, for a $π$-ordered ABP ($π$-OABP), for any directed path $p$ from source to sink, a variable can appear at most once on $p$, and the order in which variables appear on $p$ must respect $π$. An ABP $A$ is said to be of read $r$, if any variable appears at most $r$ times in $A$. Our main result pertains to the identity testing problem. Over any field $F$ and in the black-box model, i.e. given only query access to the polynomial, we have the following result: read $r$ $π$-OABP computable polynomials can be tested in $\DTIME[2^{O(r\log r \cdot \log^2 n \log\log n)}]$. Our next set of results investigates the computational limitations of OABPs. It is shown that any OABP computing the determinant or permanent requires size $Ω(2^n/n)$ and read $Ω(2^n/n^2)$. We give a multilinear polynomial $p$ in $2n+1$ variables over some specifically selected field $G$, such that any OABP computing $p$ must read some variable at least $2^n$ times. We show that the elementary symmetric polynomial of degree $r$ in $n$ variables can be computed by a size $O(rn)$ read $r$ OABP, but not by a read $(r-1)$ OABP, for any $0 < 2r-1 \leq n$. Finally, we give an example of a polynomial $p$ and two variables orders $π\neq π'$, such that $p$ can be computed by a read-once $π$-OABP, but where any $π'$-OABP computing $p$ must read some variable at least $2^n$

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arXiv2009

Deterministic Identity Testing of Read-Once Algebraic Branching Programs

In this paper we study polynomial identity testing of sums of $k$ read-once algebraic branching programs ($Σ_k$-RO-ABPs), generalizing the work in (Shpilka and Volkovich 2008,2009), who considered sums of $k$ read-once formulas ($Σ_k$-RO-formulas). We show that $Σ_k$-RO-ABPs are strictly more powerful than $Σ_k$-RO-formulas, for any $k \leq \lfloor n/2\rfloor$, where $n$ is the number of variables. We obtain the following results: 1) Given free access to the RO-ABPs in the sum, we get a deterministic algorithm that runs in time $O(k^2n^7s) + n^{O(k)}$, where $s$ bounds the size of any largest RO-ABP given on the input. This implies we have a deterministic polynomial time algorithm for testing whether the sum of a constant number of RO-ABPs computes the zero polynomial. 2) Given black-box access to the RO-ABPs computing the individual polynomials in the sum, we get a deterministic algorithm that runs in time $k^2n^{O(\log n)} + n^{O(k)}$. 3) Finally, given only black-box access to the polynomial computed by the sum of the $k$ RO-ABPs, we obtain an $n^{O(k + \log n)}$ time deterministic algorithm.

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