Sagnik Mukhopadhyay

Lower Bounds for Elimination via Weak Regularity

We consider the problem of elimination in communication complexity, that was first raised by Ambainis et al. and later studied by Beimel et al. for its connection to the famous direct sum question. In this problem, let f: {0,1}^2n -> {0,1} be any boolean function. Alice and Bob get k inputs x_1, ..., x_k and y_1, ..., y_k respectively, with x_i,y_i in {0,1}^n. They want to output a k-bit vector v, such that there exists one index i for which v_i is not equal f(x_i,y_i). We prove a general result lower bounding the randomized communication complexity of the elimination problem for f using its discrepancy. Consequently, we obtain strong lower bounds for the functions Inner-Product and Greater-Than, that work for exponentially larger values of k than the best previous bounds. To prove our result, we use a pseudo-random notion called regularity that was first used by Raz and Wigderson. We show that functions with small discrepancy are regular. We also observe that a weaker notion, that we call weak-regularity, already implies hardness of elimination. Finally, we give a different proof, borrowing ideas from Viola, to show that Greater-Than is weakly regular.

Simulation beats richness: new data-structure lower bounds

We develop a new technique for proving lower bounds in the setting of asymmetric communication, a model that was introduced in the famous works of Miltersen (STOC’94) and Miltersen, Nisan, Safra and Wigderson (STOC’95). At the core of our technique is the first simulation theorem in the asymmetric setting, where Alice gets a p × n matrix x over F2 and Bob gets a vector y ∈ F2n. Alice and Bob need to evaluate f(x· y) for a Boolean function f: {0,1}p → {0,1}. Our simulation theorems show that a deterministic/randomized communication protocol exists for this problem, with cost C· n for Alice and C for Bob, if and only if there exists a deterministic/randomized *parity decision tree* of cost Θ(C) for evaluating f. As applications of this technique, we obtain the following results: 1. The first strong lower-bounds against randomized data-structure schemes for the Vector-Matrix-Vector product problem over F2. Moreover, our method yields strong lower bounds even when the data-structure scheme has tiny advantage over random guessing. 2. The first lower bounds against randomized data-structures schemes for two natural Boolean variants of Orthogonal Vector Counting. 3. We construct an asymmetric communication problem and obtain a deterministic lower-bound for it which is provably better than any lower-bound that may be obtained by the classical Richness Method of Miltersen et al. (STOC ’95). This seems to be the first known limitation of the Richness Method in the context of proving deterministic lower bounds.

Separation Between Deterministic and Randomized Query Complexity

Saks and Wigderson [in Proceedings of the

Towards Better Separation between Deterministic and Randomized Query Complexity

27

Tribes Is Hard in the Message Passing Model

th FOCS, IEEE Computer Society, Los Alamitos, CA, 1986, pp. 29--38] conjectured that

Simulation Theorems via Pseudorandom Properties.

R_0(f) = \Omega(D(f)^{0.753\ldots})

Composition and Simulation Theorems via Pseudo-random Properties.

for all Boolean functions

Towards Better Separation between Deterministic and Randomized Query Complexity.

f...