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Jaikumar Radhakrishnan

Publications and source records attributed to Jaikumar Radhakrishnan.

At least 19 recordsLinked to original sources

Coupled dynamical phase transitions in driven disk packings

Under the influence of oscillatory shear, a mono-layer of frictional granular disks exhibits two dynamical phase transitions: a transition from an initially disordered state to an ordered crystalline state, and a dynamic active-absorbing phase transition. Although there is no reason, {\it a-priori}, for these to be at the same critical point, they are. The transitions may also be characterized by the disk trajectories, which are non-trivial loops breaking time-reversal invariance.

cond-mat.soft

Set membership with two classical and quantum bit probes

We consider the following problem: Given a set S of at most n elements from a universe of size m, represent it in memory as a bit string so that membership queries of the form "Is x in S?" can be answered by making at most t probes into the bit string. Let s(m,n,t) be the minimum number of bits needed by any such scheme. We obtain new upper bounds for s(m,n,t=2), which match or improve all the previously known bounds. We also consider the quantum version of this problem and obtain improved upper bounds.

cs.DS

Generalized Parametric Path Problems

Parametric path problems arise independently in diverse domains, ranging from transportation to finance, where they are studied under various assumptions. We formulate a general path problem with relaxed assumptions, and describe how this formulation is applicable in these domains. We study the complexity of the general problem, and a variant of it where preprocessing is allowed. We show that when the parametric weights are linear functions, algorithms remain tractable even under our relaxed assumptions. Furthermore, we show that if the weights are allowed to be non-linear, the problem becomes NP-hard. We also study the mutli-dimensional version of the problem where the weight functions are parameterized by multiple parameters. We show that even with two parameters, the problem is NP-hard.

cs.DS

Parametric Shortest Paths in Planar Graphs

We construct a family of planar graphs $\{G_n\}_{n\geq 4}$, where $G_n$ has $n$ vertices including a source vertex $s$ and a sink vertex $t$, and edge weights that change linearly with a parameter $\lambda$ such that, as $\lambda$ varies in $(-\infty,+\infty)$, the piece-wise linear cost of the shortest path from $s$ to $t$ has $n^{\Omega(\log n)}$ pieces. This shows that lower bounds obtained earlier by Carstensen (1983) and Mulmuley \& Shah (2001) for general graphs also hold for planar graphs, thereby refuting a conjecture of Nikolova (2009). Gusfield (1980) and Dean (2009) showed that the number of pieces for every $n$-vertex graph with linear edge weights is $n^{\log n + O(1)}$. We generalize this result in two ways. (i) If the edge weights vary as a polynomial of degree at most $d$, then the number of pieces is $n^{\log n + (\alpha(n)+O(1))^d}$, where $\alpha(n)$ is the slow growing inverse Ackermann function. (ii) If the edge weights are linear forms of three parameters, then the number of pieces, appropriately defined for $\mathbb{R}^3$, is $n^{(\log n)^2+O(\log n)}$.

cs.CC

Minimizing Branching Vertices in Distance-preserving Subgraphs

It is $\mathsf{NP}$-hard to determine the minimum number of branching vertices needed in a single-source distance-preserving subgraph of an undirected graph. We show that this problem can be solved in polynomial time if the input graph is an interval graph. In earlier work, it was shown that every interval graph with $k$ terminal vertices admits an all-pairs distance-preserving subgraph with $O(k\log k)$ branching vertices. We consider graphs that can be expressed as the strong product of two interval graphs, and present a polynomial time algorithm that takes such a graph with $k$ terminals as input, and outputs an all-pairs distance-preserving subgraph of it with $O(k^2)$ branching vertices. This bound is tight.

cs.DS

Bounds on the Zero-Error List-Decoding Capacity of the $q/(q-1)$ Channel

We consider the problem of determining the zero-error list-decoding capacity of the $q/(q-1)$ channel studied by Elias (1988). The $q/(q-1)$ channel has input and output alphabet consisting of $q$ symbols, say, $Q = \{x_1,x_2,\ldots, x_q\}$; when the channel receives an input $x \in Q$, it outputs a symbol other than $x$ itself. Let $n(m,q,\ell)$ be the smallest $n$ for which there is a code $C \subseteq Q^n$ of $m$ elements such that for every list $w_1, w_2, \ldots, w_{\ell+1}$ of distinct code-words from $C$, there is a coordinate $j \in [n]$ that satisfies $\{w_1[j], w_2[j], \ldots, w_{\ell+1}[j]\} = Q$. We show that for $\epsilon<1/6$, for all large $q$ and large enough $m$, $n(m,q, \epsilon q\ln{q}) \geq \Omega(\exp{(q^{1-6\epsilon}/8)}\log_2{m})$. The lower bound obtained by Fredman and Koml\'{o}s (1984) for perfect hashing implies that $n(m,q,q-1) = \exp(\Omega(q)) \log_2 m$; similarly, the lower bound obtained by K\"{o}rner (1986) for nearly-perfect hashing implies that $n(m,q,q) = \exp(\Omega(q)) \log_2 m$. These results show that the zero-error list-decoding capacity of the $q/(q-1)$ channel with lists of size at most $q$ is exponentially small. Extending these bounds, Chakraborty et al. (2006) showed that the capacity remains exponentially small even if the list size is allowed to be as large as $1.58q$. Our result implies that the zero-error list-decoding capacity of the $q/(q-1)$ channel with list size $\epsilon q$ for $\epsilon<1/6$ is $\exp{(\Omega(q^{1-6\epsilon}))}$. This resolves the conjecture raised by Chakraborty et al. (2006) about the zero-error list-decoding capcity of the $q/(q-1)$ channel at larger list sizes.

cs.IT

Distance-preserving Subgraphs of Interval Graphs

We consider the problem of finding small distance-preserving subgraphs of undirected, unweighted interval graphs with $k$ terminal vertices. To start with, we show that finding an optimal distance-preserving subgraph is $\mathsf{NP}$-hard for general graphs. Then, we show that every interval graph admits a subgraph with $O(k)$ branching vertices that approximates pairwise terminal distances up to an additive term of $+1$. We also present an interval graph $G_{\mathrm{int}}$ for which the $+1$ approximation is necessary to obtain the $O(k)$ upper bound on the number of branching vertices. In particular, any distance-preserving subgraph of $G_{\mathrm{int}}$ has $\Omega(k\log k)$ branching vertices. Furthermore, we prove that every interval graph admits a distance-preserving subgraph with $O(k\log k)$ branching vertices, implying that the $\Omega(k\log k)$ lower bound for interval graphs is tight. To conclude, we show that there exists an interval graph such that every optimal distance-preserving subgraph of it has $O(k)$ branching vertices and $\Omega(k\log k)$ branching edges, thereby providing a separation between branching vertices and branching edges. The $O(k)$ bound for distance-approximating subgraphs follows from a na\"ive analysis of shortest paths in interval graphs. $G_{\mathrm{int}}$ is constructed using bit-reversal permutation matrices. The $O(k\log k)$ bound for distance-preserving subgraphs uses a divide-and-conquer approach. Finally, the separation between branching vertices and branching edges employs Hansel's lemma for graph covering.

cs.DS

One-Shot Private Classical Capacity of Quantum Wiretap Channel: Based on one-shot quantum covering lemma

In this work we study the problem of communication over the quantum wiretap channel. For this channel there are three parties Alice (sender), Bob (legitimate receiver) and Eve (eavesdropper). We obtain upper and lower bounds on the amount of information Alice can communicate to Bob such that Eve gets to know as little information as possible about the transmitted messages. Our bounds are in terms of quantum hypothesis testing divergence and smooth max quantum relative entropy. To obtain our result we prove a one-shot version of the quantum covering lemma along with operator Chernoff bound for non-square matrices.

quant-ph

Set membership with non-adaptive bit probes

We consider the non-adaptive bit-probe complexity of the set membership problem, where a set S of size at most n from a universe of size m is to be represented as a short bit vector in order to answer membership queries of the form "Is x in S?" by non-adaptively probing the bit vector at t places. Let s_N(m,n,t) be the minimum number of bits of storage needed for such a scheme. In this work, we show existence of non-adaptive and adaptive schemes for a range of t that improves an upper bound of Buhrman, Miltersen, Radhakrishnan and Srinivasan (2002) on s_N(m,n,t). For three non-adaptive probes, we improve the previous best lower bound on s_N(m,n,3) by Alon and Feige (2009).

cs.DS

The zero-error randomized query complexity of the pointer function

The pointer function of G{\"{o}}{\"{o}}s, Pitassi and Watson \cite{DBLP:journals/eccc/GoosP015a} and its variants have recently been used to prove separation results among various measures of complexity such as deterministic, randomized and quantum query complexities, exact and approximate polynomial degrees, etc. In particular, the widest possible (quadratic) separations between deterministic and zero-error randomized query complexity, as well as between bounded-error and zero-error randomized query complexity, have been obtained by considering {\em variants}~\cite{DBLP:journals/corr/AmbainisBBL15} of this pointer function. However, as was pointed out in \cite{DBLP:journals/corr/AmbainisBBL15}, the precise zero-error complexity of the original pointer function was not known. We show a lower bound of $\widetilde{\Omega}(n^{3/4})$ on the zero-error randomized query complexity of the pointer function on $\Theta(n \log n)$ bits; since an $\widetilde{O}(n^{3/4})$ upper bound is already known \cite{DBLP:conf/fsttcs/MukhopadhyayS15}, our lower bound is optimal up to a factor of $\polylog\, n$.

cs.CC

Hypergraph Two-Coloring in the Streaming Model

We consider space-efficient algorithms for two-coloring $n$-uniform hypergraphs $H=(V,E)$ in the streaming model, when the hyperedges arrive one at a time. It is known that any such hypergraph with at most $0.7 \sqrt{\frac{n}{\ln n}} 2^n$ hyperedges has a two-coloring [Radhakrishnan & Srinivasan, RSA, 2000], which can be found deterministically in polynomial time, if allowed full access to the input. 1. Let $s^D(v, q, n)$ be the minimum space used by a deterministic one-pass streaming algorithm that on receiving an $n$-uniform hypergraph $H$ on $v$ vertices and $q$ hyperedges produces a proper two-coloring of $H$. We show that $s^D(n^2, q, n) = \Omega(q/n)$ when $q \leq 0.7 \sqrt{\frac{n}{\ln n}} 2^n$, and $s^D(n^2, q, n) = \Omega(\sqrt{\frac{1}{n\ln n}} 2^n)$ otherwise. 2. Let $s^R(v, q,n)$ be the minimum space used by a randomized one-pass streaming algorithm that on receiving an $n$-uniform hypergraph $H$ on $v$ vertices and $q$ hyperedges with high probability produces a proper two-coloring of $H$ (or declares failure). We show that $s^R(v, \frac{1}{10}\sqrt{\frac{n}{\ln n}} 2^n, n) = O(v \log v)$ by giving an efficient randomized streaming algorithm. The above results are inspired by the study of the number $q(n)$, the minimum possible number of hyperedges in a $n$-uniform hypergraph that is not two-colorable. It is known that $q(n) = \Omega(\sqrt{\frac{n}{\ln n}})$ [Radhakrishnan-Srinivasan] and $ q(n)= O(n^2 2^n)$ [Erdos, 1963]. Our first result shows that no space-efficient deterministic streaming algorithm can match the performance of the offline algorithm of Radhakrishnan and Srinivasan; the second result shows that there is, however, a space-efficient randomized streaming algorithm for the task.

cs.DS

Partition bound is quadratically tight for product distributions

Let $f : \{0,1\}^n \times \{0,1\}^n \rightarrow \{0,1\}$ be a 2-party function. For every product distribution $\mu$ on $\{0,1\}^n \times \{0,1\}^n$, we show that $$\mathsf{CC}^\mu_{0.49}(f) = O\left(\left(\log \mathsf{prt}_{1/8}(f) \cdot \log \log \mathsf{prt}_{1/8}(f)\right)^2\right),$$ where $\mathsf{CC}^\mu_\varepsilon(f)$ is the distributional communication complexity of $f$ with error at most $\varepsilon$ under the distribution $\mu$ and $\mathsf{prt}_{1/8}(f)$ is the {\em partition bound} of $f$, as defined by Jain and Klauck [{\em Proc. 25th CCC}, 2010]. We also prove a similar bound in terms of $\mathsf{IC}_{1/8}(f)$, the {\em information complexity} of $f$, namely, $$\mathsf{CC}^\mu_{0.49}(f) = O\left(\left(\mathsf{IC}_{1/8}(f) \cdot \log \mathsf{IC}_{1/8}(f)\right)^2\right).$$ The latter bound was recently and independently established by Kol [{\em Proc. 48th STOC}, 2016] using a different technique. We show a similar result for query complexity under product distributions. Let $g : \{0,1\}^n \rightarrow \{0,1\}$ be a function. For every bit-wise product distribution $\mu$ on $\{0,1\}^n$, we show that $$\mathsf{QC}^\mu_{0.49}(g) = O\left(\left( \log \mathsf{qprt}_{1/8}(g) \cdot \log \log\mathsf{qprt}_{1/8}(g) \right)^2 \right),$$ where $\mathsf{QC}^\mu_{\varepsilon}(g)$ is the distributional query complexity of $f$ with error at most $\varepsilon$ under the distribution $\mu$ and $\mathsf{qprt}_{1/8}(g))$ is the {\em query partition bound} of the function $g$. Partition bounds were introduced (in both communication complexity and query complexity models) to provide LP-based lower bounds for randomized communication complexity and randomized query complexity. Our results demonstrate that these lower bounds are polynomially tight for {\em product} distributions.

cs.CC

Coordination Complexity: Small Information Coordinating Large Populations

We initiate the study of a quantity that we call coordination complexity. In a distributed optimization problem, the information defining a problem instance is distributed among $n$ parties, who need to each choose an action, which jointly will form a solution to the optimization problem. The coordination complexity represents the minimal amount of information that a centralized coordinator, who has full knowledge of the problem instance, needs to broadcast in order to coordinate the $n$ parties to play a nearly optimal solution. We show that upper bounds on the coordination complexity of a problem imply the existence of good jointly differentially private algorithms for solving that problem, which in turn are known to upper bound the price of anarchy in certain games with dynamically changing populations. We show several results. We fully characterize the coordination complexity for the problem of computing a many-to-one matching in a bipartite graph by giving almost matching lower and upper bounds.Our upper bound in fact extends much more generally, to the problem of solving a linearly separable convex program. We also give a different upper bound technique, which we use to bound the coordination complexity of coordinating a Nash equilibrium in a routing game, and of computing a stable matching.

cs.DS

Set Membership with a Few Bit Probes

We consider the bit-probe complexity of the set membership problem, where a set S of size at most n from a universe of size m is to be represented as a short bit vector in order to answer membership queries of the form "Is x in S?" by adaptively probing the bit vector at t places. Let s(m,n,t) be the minimum number of bits of storage needed for such a scheme. Several recent works investigate s(m,n,t) for various ranges of the parameter; we obtain improvements over some of the bounds shown by Buhrman, Miltersen, Radhakrishnan, and Srinivasan (2002) and Alon and Feige (2009).

cs.DS

A Sampling Technique of Proving Lower Bounds for Noisy Computations

We present a technique of proving lower bounds for noisy computations. This is achieved by a theorem connecting computations on a kind of randomized decision trees and sampling based algorithms. This approach is surprisingly powerful, and applicable to several models of computation previously studied. As a first illustration we show how all the results of Evans and Pippenger (SIAM J. Computing, 1999) for noisy decision trees, some of which were derived using Fourier analysis, follow immediately if we consider the sampling-based algorithms that naturally arise from these decision trees. Next, we show a tight lower bound of $\Omega(N \log\log N)$ on the number of transmissions required to compute several functions (including the parity function and the majority function) in a network of $N$ randomly placed sensors, communicating using local transmissions, and operating with power near the connectivity threshold. This result considerably simplifies and strengthens an earlier result of Dutta, Kanoria Manjunath and Radhakrishnan (SODA 08) that such networks cannot compute the parity function reliably with significantly fewer than $N\log \log N$ transmissions. The lower bound for parity shown earlier made use of special properties of the parity function and is inapplicable, e.g., to the majority function. In this paper, we use our approach to develop an interesting connection between computation of boolean functions on noisy networks that make few transmissionss, and algorithms that work by sampling only a part of the input. It is straightforward to verify that such sampling-based algorithms cannot compute the majority function.

cs.CC

How Hard is Computing Parity with Noisy Communications?

We show a tight lower bound of $\Omega(N \log\log N)$ on the number of transmissions required to compute the parity of $N$ input bits with constant error in a noisy communication network of $N$ randomly placed sensors, each having one input bit and communicating with others using local transmissions with power near the connectivity threshold. This result settles the lower bound question left open by Ying, Srikant and Dullerud (WiOpt 06), who showed how the sum of all the $N$ bits can be computed using $O(N \log\log N)$ transmissions. The same lower bound has been shown to hold for a host of other functions including majority by Dutta and Radhakrishnan (FOCS 2008). Most works on lower bounds for communication networks considered mostly the full broadcast model without using the fact that the communication in real networks is local, determined by the power of the transmitters. In fact, in full broadcast networks computing parity needs $\theta(N)$ transmissions. To obtain our lower bound we employ techniques developed by Goyal, Kindler and Saks (FOCS 05), who showed lower bounds in the full broadcast model by reducing the problem to a model of noisy decision trees. However, in order to capture the limited range of transmissions in real sensor networks, we adapt their definition of noisy decision trees and allow each node of the tree access to only a limited part of the input. Our lower bound is obtained by exploiting special properties of parity computations in such noisy decision trees.

cs.DC

One-shot Marton inner bound for classical-quantum broadcast channel

We consider the problem of communication over a classical-quantum broadcast channel with one sender and two receivers. Generalizing the classical inner bounds shown by Marton and the recent quantum asymptotic version shown by Savov and Wilde, we obtain one-shot inner bounds in the quantum setting. Our bounds are stated in terms of smooth min and max Renyi divergences. We obtain these results using a different analysis of the random codebook argument and employ a new one-shot classical mutual covering argument based on rejection sampling. These results give a full justification of the claims of Savov and Wilde in the classical-quantum asymptotic iid setting; the techniques also yield similar bounds in the information spectrum setting.

cs.IT

On Zarankiewicz Problem and Depth-Two Superconcentrators

We show tight necessary and sufficient conditions on the sizes of small bipartite graphs whose union is a larger bipartite graph that has no large bipartite independent set. Our main result is a common generalization of two classical results in graph theory: the theorem of K\H{o}v\'{a}ri, S\'{o}s and Tur\'{a}n on the minimum number of edges in a bipartite graph that has no large independent set, and the theorem of Hansel (also Katona and Szemer\'{e}di, Krichevskii) on the sum of the sizes of bipartite graphs that can be used to construct a graph (non-necessarily bipartite) that has no large independent set. As an application of our results, we show how they unify the underlying combinatorial principles developed in the proof of tight lower bounds for depth-two superconcentrators.

cs.DM