Many blockchain systems today, including Bitcoin, rely on Proof of Work (PoW). Proof of work is crucial to the liveness and security of cryptocurrencies. The assumption when using PoW is that a lot of trial and error is required on average before a valid block is generated. One of the main concerns raised with regard to this kind of system is the inherent need to "waste" energy on "meaningless" problems. In fact, the Bitcoin system is believed to consume more electricity than several small countries. In this work we formally define three properties that are necessary for wasteless PoW systems: (1) solve "meaningful" problems (2) solve them efficiently and (3) be secure against double-spend attacks. These properties aim to create an open market for problem-solving, in which miners produce solutions to problems in the most efficient way (wasteless). The security of the system stems from the economical incentive created by the demand for solutions to these problems. We analyze these properties, and deduce constraints that must apply to such PoW systems. In our main result, we conclude that under realistic assumptions, the set of allowed problems must be preimage resistant functions in order to keep the system secure and efficient.

A recent work by [Larsen, SODA 2023] introduced a faster combinatorial alternative to Bansal’s SDP algorithm for finding a coloring $x \in \\{-1, 1\\}^n$ that approximately minimizes the discrepancy $\mathrm{disc}(A, x) := \\|{A} x \\|_{\infty}$ of a real-valued $m \times n$ matrix $A$. Larsen’s algorithm runs in $\widetilde{O}(mn^2)$ time compared to Bansal’s $\widetilde{O}(mn^{4.5})$-time algorithm, with a slightly weaker logarithmic approximation ratio in terms of the hereditary discrepancy of $A$ [Bansal, FOCS 2010]. We present a combinatorial $\widetilde{O}(\mathrm{nnz}(A) + n^3)$-time algorithm with the same approximation guarantee as Larsen’s, optimal for tall matrices where $m = \mathrm{poly}(n)$. Using a more intricate analysis and fast matrix multiplication, we further achieve a runtime of $\widetilde{O}(\mathrm{nnz}(A) + n^{2.53})$, breaking the cubic barrier for square matrices and surpassing the limitations of linear-programming approaches [Eldan and Singh, RS&A 2018]. Our algorithm relies on two key ideas: (i) a new sketching technique for finding a projection matrix with a short $\ell_2$-basis using implicit leverage-score sampling, and (ii) a data structure for efficiently implementing the iterative Edge-Walk partial-coloring algorithm [Lovett and Meka, SICOMP 2015], and using an alternative analysis to enable “lazy” batch updates with low-rank corrections. Our results nearly close the computational gap between real-valued and binary matrices, for which input-sparsity time coloring was recently obtained by [Jain, Sah and Sawhney, SODA 2023].

We present a general framework for designing efficient data structures for high-dimensional pattern-matching problems ($\exists \;? i\in[n], f(x_i,y)=1$) through communication models in which $f(x,y)$ admits sublinear communication protocols with exponentially-small error. Specifically, we reduce the data structure problem to the Unambiguous Arthur-Merlin (UAM) communication complexity of $f(x,y)$ under product distributions. We apply our framework to the Partial Match problem (a.k.a, matching with wildcards), whose underlying communication problem is sparse set-disjointness. When the database consists of $n$ points in dimension $d$, and the number of $\star$'s in the query is at most $w = c\log n \;(\ll d)$, the fastest known linear-space data structure (Cole, Gottlieb and Lewenstein, STOC'04) had query time $t \approx 2^w = n^c$, which is nontrivial only when $c<1$. By contrast, our framework produces a data structure with query time $n^{1-1/(c \log^2 c)}$ and space close to linear. To achieve this, we develop a one-sided $ε$-error communication protocol for Set-Disjointness under product distributions with $\tildeΘ(\sqrt{d\log(1/ε)})$ complexity, improving on the classical result of Babai, Frankl and Simon (FOCS'86). Building on this protocol, we show that the Unambiguous AM communication complexity of $w$-Sparse Set-Disjointness with $ε$-error under product distributions is $\tilde{O}(\sqrt{w \log(1/ε)})$, independent of the ambient dimension $d$, which is crucial for the Partial Match result. Our framework sheds further light on the power of data-dependent data structures, which is instrumental for reducing to the (much easier) case of product distributions.

We prove direct-sum theorems for Wilber's two lower bounds [Wilber, FOCS'86] on the cost of access sequences in the binary search tree (BST) model. These bounds are central to the question of dynamic optimality [Sleator and Tarjan, JACM'85]: the Alternation bound is the only bound to have yielded online BST algorithms beating $\log n$ competitive ratio, while the Funnel bound has repeatedly been conjectured to exactly characterize the cost of executing an access sequence using the optimal tree [Wilber, FOCS'86, Kozma'16], and has been explicitly linked to splay trees [Levy and Tarjan, SODA'19]. Previously, the direct-sum theorem for the Alternation bound was known only when approximation was allowed [Chalermsook, Chuzhoy and Saranurak, APPROX'20, ToC'24]. We use these direct-sum theorems to amplify the sequences from [Lecomte and Weinstein, ESA'20] that separate between Wilber's Alternation and Funnel bounds, increasing the Alternation and Funnel bounds while optimally maintaining the separation. As a corollary, we show that Tango trees [Demaine et al., FOCS'04] are optimal among any BST algorithms that charge their costs to the Alternation bound. This is true for any value of the Alternation bound, even values for which Tango trees achieve a competitive ratio of $o(\log \log n)$ instead of the default $O(\log \log n)$. Previously, the optimality of Tango trees was shown only for a limited range of Alternation bound [Lecomte and Weinstein, ESA'20].

Proving superlogarithmic data structure lower bounds in the static group model has been a fundamental challenge in computational geometry since the early '80s. We prove a polynomial ($n^{\Omega(1)}$) lower bound for an explicit range counting problem of $n^3$ convex polygons in $\mathbb{R}^2$ (each with $n^{\tilde{O}(1)}$ facets/semialgebraic complexity), against linear storage arithmetic data structures in the group model. Our construction and analysis are based on a combination of techniques in Diophantine approximation, pseudorandomness, and compressed sensing—in particular, on the existence and partial derandomization of optimal binary compressed sensing matrices in the polynomial sparsity regime ($k = n^{1-\delta}$). As a byproduct, this establishes a (logarithmic) separation between compressed sensing matrices and the stronger RIP property.

We study the problem of interpolating a noisy Fourier-sparse signal in the time duration $[0, T]$ from noisy samples in the same range, where the ground truth signal can be any k-Fourier-sparse signal with band-limit $[-F, F]$. Our main result is an efficient Fourier Interpolation algorithm that improves the previous best algorithm by [Chen, Kane, Price, and Song, FOCS 2016] in the following three aspects:•The sample complexity is improved from $\widetilde{O}\left(k^{51}\right)$ to $\widetilde{O}\left(k^{4}\right)$.•The time complexity is improved from $\widetilde{O}\left(k^{10 \omega+40}\right)$ to $\widetilde{O}\left(k^{4 \omega}\right)$.•The output sparsity is improved from $\widetilde{O}\left(k^{10}\right)$ to $\widetilde{O}\left(k^{4}\right)$. Here, $\omega$ denotes the exponent of fast matrix multiplication. The state-of-the-art sample complexity of this problem is $\sim k^{4}$, but was only known to be achieved by an exponential-time algorithm. Our algorithm uses the same number of samples but has a polynomial runtime, laying the groundwork for an efficient Fourier Interpolation algorithm.The centerpiece of our algorithm is a new spectral analysis tool-the Signal Equivalent Method-which utilizes the structure of Fourier signals to establish nearly-optimal energy properties, and is the key for efficient and accurate frequency estimation. We use this method, along with a new sufficient condition for frequency recovery (a new high SNR band condition), to design a cheap algorithm for estimating “significant” frequencies within a narrow range. Together with a signal estimation algorithm, we obtain a new Fourier Interpolation algorithm for reconstructing the ground-truth signal.

We settle the complexity of dynamic least-squares regression (LSR), where rows and labels $\left(\mathbf{A}^{(t)}, \mathbf{b}^{(t)}\right)$ can be adaptively inserted and/or deleted, and the goal is to efficiently maintain an $\epsilon$-approximate solution to $\min _{\mathbf{x}^{(t)}}\left\|\mathbf{A}^{(t)} \mathbf{x}^{(t)}-\mathbf{b}^{(t)}\right\|_{2}$ for all $t \in[T]$. We prove sharp separations $\left(d^{2-o(1)}\right.$ vs. $\left.\sim d\right)$ between the amortized update time of: (i) Fully vs. Partially dynamic 0.01-LSR; (ii) High vs. low-accuracy LSR in the partially-dynamic (insertion-only) setting.Our lower bounds follow from a gap-amplification reduction–reminiscent of iterative refinement-from the exact version of the Online Matrix Vector Conjecture (OMv) [HKNS15], to constant approximate OMv over the reals, where the i-th online product $\mathrm{Hv}^{(i)}$ only needs to be computed to 0.1 -relative error. All previous fine-grained reductions from OMv to its approximate versions only show hardness for inverse polynomial approximation $\epsilon=$ $n^{-\omega(1)}$ (additive or multiplicative). This result is of independent interest in fine-grained complexity and for the investigation of the OMv Conjecture, which is still widely open.

We present a faster interior-point method for optimizing sum-of-squares (SOS) polynomials, which are a central tool in polynomial optimization and capture convex programming in the Lasserre hierarchy. Let p = ∑_i q²_i be an n-variate SOS polynomial of degree 2d. Denoting by L : = binom(n+d,d) and U : = binom(n+2d,2d) the dimensions of the vector spaces in which q_i’s and p live respectively, our algorithm runs in time Õ(LU^{1.87}). This is polynomially faster than state-of-art SOS and semidefinite programming solvers [Jiang et al., 2020; Huang et al., 2021; Papp and Yildiz, 2019], which achieve runtime Õ(L^{0.5} min{U^{2.37}, L^{4.24}}). The centerpiece of our algorithm is a dynamic data structure for maintaining the inverse of the Hessian of the SOS barrier function under the polynomial interpolant basis [Papp and Yildiz, 2019], which efficiently extends to multivariate SOS optimization, and requires maintaining spectral approximations to low-rank perturbations of elementwise (Hadamard) products. This is the main challenge and departure from recent IPM breakthroughs using inverse-maintenance, where low-rank updates to the slack matrix readily imply the same for the Hessian matrix.

In the \emph{Distance Oracle} problem, the goal is to preprocess $n$ vectors $x_1, x_2, \cdots, x_n$ in a $d$-dimensional normed space $(\mathbb{X}^d, \| \cdot \|_l)$ into a cheap data structure, so that given a query vector $q \in \mathbb{X}^d$, all distances $\| q - x_i \|_l$ to the data points $\{x_i\}_{i\in [n]}$ can be quickly approximated (faster than the trivial $\sim nd$ query time). This primitive is a basic subroutine in machine learning, data mining and similarity search applications. In the case of $\ell_p$ norms, the problem is well understood, and optimal data structures are known for most values of $p$. Our main contribution is a fast $(1\pm \varepsilon)$ distance oracle for \emph{any symmetric} norm $\|\cdot\|_l$. This class includes $\ell_p$ norms and Orlicz norms as special cases, as well as other norms used in practice, e.g. top-$k$ norms, max-mixture and sum-mixture of $\ell_p$ norms, small-support norms and the box-norm. We propose a novel data structure with $\tilde{O}(n (d + \mathrm{mmc}(l)^2 ) )$ preprocessing time and space, and $t_q = \tilde{O}(d + n \cdot \mathrm{mmc}(l)^2)$ query time, where $\mathrm{mmc}(l)$ is a complexity-measure (modulus) of the symmetric norm under consideration. When $l = \ell_{p}$ , this runtime matches the aforementioned state-of-art oracles.

The slow convergence rate and pathological curvature issues of first-order gradient methods for training deep neural networks, initiated an ongoing effort for developing faster $\mathit{second}$-$\mathit{order}$ optimization algorithms beyond SGD, without compromising the generalization error. Despite their remarkable convergence rate ($\mathit{independent}$ of the training batch size $n$), second-order algorithms incur a daunting slowdown in the $\mathit{cost}$ $\mathit{per}$ $\mathit{iteration}$ (inverting the Hessian matrix of the loss function), which renders them impractical. Very recently, this computational overhead was mitigated by the works of [ZMG19,CGH+19}, yielding an $O(mn^2)$-time second-order algorithm for training two-layer overparametrized neural networks of polynomial width $m$. We show how to speed up the algorithm of [CGH+19], achieving an $\tilde{O}(mn)$-time backpropagation algorithm for training (mildly overparametrized) ReLU networks, which is near-linear in the dimension ($mn$) of the full gradient (Jacobian) matrix. The centerpiece of our algorithm is to reformulate the Gauss-Newton iteration as an $\ell_2$-regression problem, and then use a Fast-JL type dimension reduction to $\mathit{precondition}$ the underlying Gram matrix in time independent of $M$, allowing to find a sufficiently good approximate solution via $\mathit{first}$-$\mathit{order}$ conjugate gradient. Our result provides a proof-of-concept that advanced machinery from randomized linear algebra -- which led to recent breakthroughs in $\mathit{convex}$ $\mathit{optimization}$ (ERM, LPs, Regression) -- can be carried over to the realm of deep learning as well.

The fastest known LP solver for general (dense) linear programs is due to [Cohen, Lee and Song’19] and runs in O*(nω +n2.5−α/2 + n2+1/6) time. A number of follow-up works [Lee, Song and Zhang’19, Brand’20, Song and Yu’20] obtain the same complexity through different techniques, but none of them can go below n2+1/6, even if ω=2. This leaves a polynomial gap between the cost of solving linear systems (nω) and the cost of solving linear programs, and as such, improving the n2+1/6 term is crucial toward establishing an equivalence between these two fundamental problems. In this paper, we reduce the running time to O*(nω +n2.5−α/2 + n2+1/18) where ω and α are the fast matrix multiplication exponent and its dual. Hence, under the common belief that ω ≈ 2 and α ≈ 1, our LP solver runs in O*(n2.055) time instead of O*(n2.16).

In FOCS 1986, Wilber proposed two combinatorial lower bounds on the operational cost of any binary search tree (BST) for a given access sequence $X \in [n]^m$. Both bounds play a central role in the ongoing pursuit of the dynamic optimality conjecture (Sleator and Tarjan, 1985), but their relationship remained unknown for more than three decades. We show that Wilber's Funnel bound dominates his Alternation bound for all $X$, and give a tight $Θ(\lg\lg n)$ separation for some $X$, answering Wilber's conjecture and an open problem of Iacono, Demaine et. al. The main ingredient of the proof is a new "symmetric" characterization of Wilber's Funnel bound, which proves that it is invariant under rotations of $X$. We use this characterization to provide initial indication that the Funnel bound matches the Independent Rectangle bound (Demaine et al., 2009), by proving that when the Funnel bound is constant, $\mathsf{IRB}_{\diagup\hspace{-.6em}\square}$ is linear. To the best of our knowledge, our results provide the first progress on Wilber's conjecture that the Funnel bound is dynamically optimal (1986).

Proving super-logarithmic data structure lower bounds in the static group model has been a fundamental challenge in computational geometry since the early 80's. We prove a polynomial (nΩ(1)) lower bound for an explicit range counting problem of n3convex polygons in \mathbbR2(each with nÕ̃(1)facets/semialgebraic-complexity), against linear storage arithmetic data structures in the group model. Our construction and analysis are based on a combination of techniques in Diophantine approximation, pseudorandomness, and compressed sensing-in particular, on the existence and partial derandomization of optimal binary compressed sensing matrices in the polynomial sparsity regime (k=n1-δ). As a byproduct, this establishes a (logarithmic) separation between compressed sensing matrices and the stronger RIP property.

In 2010, Pătraşcu proposed a dynamic set-disjointness problem, known as the Multiphase problem, as a candidate for proving polynomial lower bounds on the operational time of dynamic data structures. He conjectured that any data structure for the Multiphase problem must make nεcell-probes in either update or query phases, and showed that this would imply similar unconditional lower bounds on many important dynamic data structure problems. There has been almost no progress on this conjecture in the past decade since its introduction. We show an ~Ω(√n) cell-probe lower bound on the Multiphase problem for data structures with general (adaptive) updates, and queries with unbounded but “layered” adaptivity. This result captures all known set-intersection data structures and significantly strengthens previous Multiphase lower bounds, which only captured non-adaptive data structures. Our main technical result is a communication lower bound on a 4-party variant of Pătraşcu's Number-On-Forehead Multiphase game, using information complexity techniques. We then use this result to make progress on understanding the power of nonlinear gates in networks computing linear operators, a long-standing open problem in circuit complexity and network design: We show that any depth- d circuit that computes a random m×n linear operator x→ Ax using gates of degree k (width- k DNFs) must have Ω(m·n1/2(d+k)) wires. Finally, we show that a lower bound on Pătraşcu's original NOF game would imply a polynomial wire lower bound (n1+Ω(1/d)) for circuits with arbitrary gates computing a random linear operator. This suggests that the NOF conjecture is much stronger than its data structure counterpart.

We prove an Ω(d lg n/(lg lg n)2) lower bound on the dynamic cell-probe complexity of statistically oblivious approximate-near-neighbor search (ANN) over the d-dimensional Hamming cube. For the natural setting of d = Θ(lg n), our result implies an lower bound, which is a quadratic improvement over the highest (non-oblivious) cell-probe lower bound for ANN. This is the first super-logarithmic unconditional lower bound for ANN against general (non black-box) data structures. We also show that any oblivious static data structure for decomposable search problems (like ANN) can be obliviously dynamized with O(lg n) overhead in update and query time, strengthening a classic result of Bentley and Saxe (Algorithmica, 1980).

Motivated by storage applications, we study the following data structure problem: an encoder wishes to store a collection of jointly-distributed files : = (X1, X2, …, Xn) ∼ µ which are correlated (Hµ ≤ Σi Hµ(Xi)), using as little (expected) memory as possible, such that each individual file Xi can be recovered quickly with few (ideally constant) memory accesses. In the case of independent random files, a dramatic result by Pǎtraşcu (FOCS’08) and subsequently by Dodis, Pǎtraşcu and Thorup (STOC’10) shows that it is possible to store using just a constant number of extra bits beyond the information-theoretic minimum space, while at the same time decoding each Xi in constant time. However, in the (realistic) case where the files are correlated, much weaker results are known, requiring at least Ω(n/poly lg n) extra bits for constant decoding time, even for “simple” joint distributions µ. We focus on the natural case of compressing Markov chains, i.e., storing a length-n random walk on any (possibly directed) graph G. Denoting by κ(G, n) the number of length-n walks on G, we show that there is a succinct data structure storing a random walk using lg2 κ(G, n) + O(lg n) bits of space, such that any vertex along the walk can be decoded in O(1) time on a word-RAM. If the graph is strongly connected (e.g., undirected), the space can be improved to only lg2 k(G, n) + 5 extra bits. For the harder task of matching the point-wise optimal space of the walk, i.e., the empirical entropy , we present a data structure with O(1) extra bits at the price of O(lg n) decoding time, and show that any improvement on this would lead to an improved solution on the long-standing Dictionary problem. All of our data structures support the online version of the problem with constant update and query time.

This paper proves the first superlogarithmic lower bounds on the cell probe complexity of dynamic Boolean (also known as decision) data structure problems, a long-standing milestone in data structure lower bounds. We introduce a new method for proving dynamic cell probe lower bounds and use it to prove an $\tilde{\Omega}({lg}^{1.5} \ n)$ lower bound on the operational time of a wide range of Boolean data structure problems, most notably, on the query time of dynamic range counting over $\mathbb{F}_2$ [M. Patrascu, Lower bounds for $2$-dimensional range counting, in STOC 2007, ACM, New York, 2007, pp. 40--46]. Proving an $\omega({\rm lg} \ n)$ lower bound for this problem was explicitly posed as one of five important open problems in the late Mihai Pǎtraşcu's obituary [M. Thorup, Bull. Eur. Assoc. Theor. Comput. Sci., 109 (2013), pp. 7--13]. This result also implies the first $\omega({lg} \ n)$ lower bound for the classical 2-dimensional (2D) range counting problem, one of the most fundamental data structure problems in computational geometry and spatial databases. We derive similar lower bounds for Boolean versions of dynamic polynomial evaluation and 2D rectangle stabbing, and for the (non-Boolean) problems of range selection and range median. Our technical centerpiece is a new way of “weakly” simulating dynamic data structures using efficient one-way communication protocols with small advantage over random guessing. This simulation involves a surprising excursion to low-degree (Chebyshev) polynomials which may be of independent interest, and offers an entirely new algorithmic angle on the “cell sampling” method of Panigrahy, Talwar, and Wieder [ Lower bounds on near neighbor search via metric expansion, FOCS 2010, IEEE Computer Society, Los Alamitos, CA, 2010, pp. 805--814].

Massively parallel computation (MPC) algorithms for graph problems have witnessed a resurgence of interest in recent years. Despite major progress for numerous graph problems however, the complexity of the sparse graph connectivity problem in this model has remained elusive: While classical logarithmic-round PRAM algorithms for finding connected components in any n-vertex graph have been known for more than three decades (and imply the same bounds for MPC model), no o(log n)-round MPC algorithms are known for this task with truly sublinear in n memory per machine (which is the only interesting regime for sparse graphs with O(n) edges). It is conjectured that an o(log n)-round algorithm for connectivity on general sparse graphs with n1-Ω (1) per-machine memory may not exist, a conjecture that also forms the basis for multiple conditional hardness results on the round complexity of other problems in the MPC model.

We show that static data structure lower bounds in the group (linear) model imply semi-explicit lower bounds on matrix rigidity. In particular, we prove that an explicit lower bound of t ≥ ω(log2 n) on the cell-probe complexity of linear data structures in the group model, even against arbitrarily small linear space (s= (1+)n), would already imply a semi-explicit (PNP) construction of rigid matrices with significantly better parameters than the current state of art (Alon, Panigrahy and Yekhanin, 2009). Our results further assert that polynomial (t≥ nδ) data structure lower bounds against near-optimal space, would imply super-linear circuit lower bounds for log-depth linear circuits (a four-decade open question). In the succinct space regime (s=n+o(n)), we show that any improvement on current cell-probe lower bounds in the linear model would also imply new rigidity bounds. Our results rely on a new connection between the “inner” and “outer” dimensions of a matrix (Paturi and Pudlák, 2006), and on a new reduction from worst-case to average-case rigidity, which is of independent interest.

The Burrows-Wheeler Transform (BWT) is among the most influential discoveries in text compression and DNA storage. It is a reversible preprocessing step that rearranges an n-letter string into runs of identical characters (by exploiting context regularities), resulting in highly compressible strings, and is the basis of the bzip compression program. Alas, the decoding process of BWT is inherently sequential and requires Ω(n) time even to retrieve a single character.

This paper proves the first super-logarithmic lower bounds on the cell probe complexity of dynamic boolean (a.k.a. decision) data structure problems, a long-standing milestone in data structure lower bounds.

The minrank of a directed graph G is the minimum rank of a matrix M that can be obtained from the adjacency matrix of G by switching some ones to zeros (i.e., deleting edges) and then setting all diagonal entries to one. This quantity is closely related to the fundamental information-theoretic problems of (linear) index coding (Bar-Yossef et al.), network coding (Effros et al.), and distributed storage (Mazumdar, ISIT, 2014). We prove tight bounds on the minrank of directed Erdos- Rényi random graphs G(n, p) for all regimes of p ∈ [0, 1]. In particular, for any constant p, we show that minrk(G) = Θ(n/log n) with high probability, where G is chosen from the previous best lower bound of Ω(√(n)) (Haviv and Langberg), G(n, p). This bound gives a near quadratic improvement over and partially settles an open problem raised by Lubetzky and Stav. Our lower bound matches the well-known upper bound obtained by the “clique covering" solution and settles the linear index coding problem for random knowledge graphs.

We show that, assuming the (deterministic) Exponential Time Hypothesis, distinguishing between a graph with an induced k-clique and a graph in which all k-subgraphs have density at most 1 - ∊, requires time. Our result essentially matches the quasi-polynomial algorithms of Feige and Seltser [FS97] and Barman [Bar15] for this problem, and is the first one to rule out an additive PTAS for Densest k-Subgraph. We further strengthen this result by showing that our lower bound continues to hold when, in the soundness case, even subgraphs smaller by a near-polynomial factor are assumed to be at most (1 - ∊)-dense. Our reduction is inspired by recent applications of the “birthday repetition” technique [AIM14, BKW15]. Our analysis relies on information theoretical machinery and is similar in spirit to analyzing a parallel repetition of two- prover games in which the provers may choose to answer some challenges multiple times, while completely ignoring other challenges.

One of the major open problems in complexity theory is proving superlogarithmic lower bounds on the depth of circuits (i.e., $\mathbf{P}\not\subseteq\mathbf{NC}^1$). This problem is interesting for two reasons: first, it is tightly related to understanding the power of parallel computation and of small-space computation; second, it is one of the first milestones toward proving superpolynomial circuit lower bounds. Karchmer, Raz, and Wigderson [Comput. Complexity, 5 (1995), pp. 191--204] suggested approaching this problem by proving the following conjecture: given two Boolean functions $f$ and $g$, the depth complexity of the composed function $g\diamond f$ is roughly the sum of the depth complexities of $f$ and $g$. They showed that the validity of this conjecture would imply that $\mathbf{P}\not\subseteq\mathbf{NC}^1$. As a starting point for studying the composition of functions, they introduced a relation called “the universal relation” and suggested studying the composition of universal relations. This suggestion proved fruitful, and an analogue of the Karchmer--Raz--Wigderson (KRW) conjecture for the universal relation was proved by Edmonds et al. [Comput. Complexity, 10 (2001), pp. 210--246]. An alternative proof was given later by H\aastad and Wigderson [in Advances in Computational Complexity Theory, DIMACS Ser. Discrete Math. Theoret. Comput. Sci. 13, AMS, Providence, RI, 1993, pp. 119--134]. However, studying the composition of functions seems more difficult, and the KRW conjecture is still an open question. In this work, we make a natural step in this direction, which lies between what is known and the original conjecture: we show that an analogue of the conjecture holds for the composition of a function with a universal relation.

Strategic interactions often take place in an environment rife with uncertainty. As a result, the equilibrium of a game is intimately related to the information available to its players. The \emph{signaling problem} abstracts the task faced by an informed "market maker", who must choose how to reveal information in order to effect a desirable equilibrium. In this paper, we consider two fundamental signaling problems: one for abstract normal form games, and the other for single item auctions. For the former, we consider an abstract class of objective functions which includes the social welfare and weighted combinations of players' utilities, and for the latter we restrict our attention to the social welfare objective and to signaling schemes which are constrained in the number of signals used. For both problems, we design approximation algorithms for the signaling problem which run in quasi-polynomial time under various conditions, extending and complementing the results of various recent works on the topic. Underlying each of our results is a "meshing scheme" which effectively overcomes the "curse of dimensionality" and discretizes the space of "essentially different" posterior beliefs -- in the sense of inducing "essentially different" equilibria. This is combined with an algorithm for optimally assembling a signaling scheme as a convex combination of such beliefs. For the normal form game setting, the meshing scheme leads to a convex partition of the space of posterior beliefs and this assembly procedure is reduced to a linear program, and in the auction setting the assembly procedure is reduced to submodular function maximization.

We study the two-party communication complexity of finding an approximate Brouwer fixed point of a composition of two Lipschitz functions g o f: [0,1]n→ [0,1]n, where Alice holds f and Bob holds g. We prove an exponential (in n) lower bound on the deterministic communication complexity of this problem. Our technical approach is to adapt the Raz-McKenzie simulation theorem (FOCS 1999) into geometric settings, thereby "smoothly lifting" the deterministic query lower bound for finding an approximate fixed point (Hirsch, Papadimitriou and Vavasis, Complexity 1989) from the oracle model to the two-party model. Our results also suggest an approach to the well-known open problem of proving strong lower bounds on the communication complexity of computing approximate Nash equilibria. Specifically, we show that a slightly "smoother" version of our fixed-point computation lower bound (by an absolute constant factor) would imply that: The deterministic two-party communication complexity of finding an ∈ = Ω(1/log2N)-approximate Nash equilibrium in an N × N bimatrix game (where each player knows only his own payoff matrix) is at least Nγfor some constant γ > 0. (In contrast, the nondeterministic communication complexity of this problem is only O(log6N)). ; The deterministic (Number-In-Hand) multiparty communication complexity of finding an ∈ = Ω(1)-Nash equilibrium in a k-player constant-action game is at least 2Ω(k/log k)(while the nondeterministic communication complexity is only O(k)).

This paper develops a new technique for proving amortized, randomized cell-probe lower bounds on dynamic data structure problems. We introduce a new randomized nondeterministic four-party communication model that enables "accelerated", error-preserving simulations of dynamic data structures. We use this technique to prove an Ω(n(log n/log log n)2) cell-probe lower bound for the dynamic 2D weighted orthogonal range counting problem (2D-ORC) with n/poly log n updates and n queries, that holds even for data structures with exp(-Ω̃(n)) success probability. This result not only proves the highest amortized lower bound to date, but is also tight in the strongest possible sense, as a matching upper bound can be obtained by a deterministic data structure with worst-case operational time. This is the first demonstration of a "sharp threshold" phenomenon for dynamic data structures. Our broader motivation is that cell-probe lower bounds for exponentially small success facilitate reductions from dynamic to static data structures. As a proof-of-concept, we show that a slightly strengthened version of our lower bound would imply an Ω((log n/log log n)2) lower bound for the static 3D-ORC problem with O(n logO(1) n) space. Such result would give a near quadratic improvement over the highest known static cell-probe lower bound, and break the long standing Ω(log n) barrier for static data structures.

Suppose X is a uniformly distributed n-dimensional binary vector and Y is obtained by passing X through a binary symmetric channel with crossover probability α. A recent conjecture by Courtade and Kumar postulates that I(f(X); Y ) ≤ 1 - h(α) for any Boolean function f. So far, the best known upper bound was essentially I(f(X); Y ) ≤ (1 - 2α)2. In this paper, we derive a new upper bound that holds for all balanced functions, and improves upon the best known previous bound for α > 1 over 3.

We continue the study of welfare maximization in unit-demand (matching) markets, in a distributed information model where agent's valuations are unknown to the central planner, and therefore communication is required to determine an efficient allocation. Dobzinski, Nisan and Oren (STOC'14) showed that if the market size is n, then r rounds of interaction (with logarithmic bandwidth) suffice to obtain an n1/(r+1)-approximation to the optimal social welfare. In particular, this implies that such markets converge to a stable state (constant approximation) in time logarithmic in the market size. We obtain the first multi-round lower bound for this setup. We show that even if the allowable per-round bandwidth of each agent is nε(r), the approximation ratio of any r-round (randomized) protocol is no better than Ω(n1/5r+1), implying an Ω(log log n) lower bound on the rate of convergence of the market to equilibrium. Our construction and technique may be of interest to round-communication tradeoffs in the more general setting of combinatorial auctions, for which the only known lower bound is for simultaneous (r = 1) protocols [DNO14].

The celebrated PPAD hardness result for finding an exact Nash equilibrium in a two-player game initiated a quest for finding approximate Nash equilibria efficiently, and is one of the major open questions in algorithmic game theory. We study the computational complexity of finding an ε-approximate Nash equilibrium with good social welfare. Hazan and Krauthgamer and subsequent improvements showed that finding an ε-approximate Nash equilibrium with good social welfare in a two player game and many variants of this problem is at least as hard as finding a planted clique of size O(log n) in the random graph (n, 1/2). We show that any polynomial time algorithm that finds an ε-approximate Nash equilibrium with good social welfare refutes (the worst-case) Exponential Time Hypothesis by Impagliazzo and Paturi, confirming the recent conjecture by Aaronson, Impagliazzo and Moshkovitz. Specifically, it would imply a 2Õ(n1/2) algorithm for SAT. Our lower bound matches the quasi-polynomial time algorithm by Lipton, Markakis and Mehta for solving the problem. Our key tool is a reduction from the PCP machinery to finding Nash equilibrium via free games, the framework introduced in the recent work by Aaronson, Impagliazzo and Moshkovitz. Techniques developed in the process may be useful for replacing planted clique hardness with ETH-hardness in other applications.

We introduce a novel technique which enables two players to maintain an estimate of the internal information cost of their conversation in an online fashion without revealing much extra information. We use this construction to obtain new results about communication complexity and information-theoretic privacy.

Competitive equilibrium, the central equilibrium notion in markets with indivisible goods, is based on pricing each good such that the demand for goods equals their supply and the market clears. This equilibrium notion is not guaranteed to exist beyond the narrow case of substitute goods, might result in zero revenue even when consumers value the goods highly, and overlooks the widespread practice of pricing bundles rather than individual goods. Alternative equilibrium notions proposed to address these shortcomings have either made a strong assumption on the ability to withhold supply in equilibrium, or have allowed an exponential number of prices. In this paper we study the notion of competitive bundling equilibrium – a competitive equilibrium over the market induced by partitioning the goods into bundles. Such an equilibrium is guaranteed to exist, is succinct, and satisfies the fundamental economic condition of market clearance. We establish positive welfare and revenue guarantees for this solution concept: For welfare we show that in markets with homogeneous goods, there always exists a competitive bundling equilibrium that achieves a logarithmic fraction of the optimal welfare. We also extend this result to establish nontrivial welfare guarantees for markets with heterogeneous goods. For revenue we show that in a natural class of markets for which competitive equilibrium does not guarantee positive revenue, there always exists a competitive bundling equilibrium that extracts as revenue a logarithmic fraction of the optimal welfare. Both results are tight. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

One of the major open problems in complexity theory is proving super-logarithmic lower bounds on the depth of circuits (i.e., P ⊈ NC1). This problem is interesting for two reasons: first, it is tightly related to understanding the power of parallel computation and of small-space computation; second, it is one of the first milestones toward proving super-polynomial circuit lower bounds.

We give exponentially small upper bounds on the success probability for computing the direct product of any function over any distribution using a communication protocol. Let suc(μ, f, C) denote the maximum success probability of a 2-party communication protocol for computing the boolean function f(x, y) with C bits of communication, when the inputs (x, y) are drawn from the distribution μ. Let μnbe the product distribution on n inputs and fndenote the function that computes n copies of f on these inputs. We prove that if T log3/2T ≪ (C - 1)√n and suc(μ, f, C)n, fn, T) ≤ exp(-Ω(n)). When μ is a product distribution, we prove a nearly optimal result: as long as T log2T ≪ Cn, we must have suc(μn, fn, T) ≤ exp(-Ω(n)).

We develop a new local characterization of the zero-error information complexity function for two-party communication problems, and use it to compute the exact internal and external information complexity of the 2-bit AND function: IC(AND,0) = C∧≅ 1.4923 bits, and ICext(AND,0) = log2 3 ≅ 1.5839 bits. This leads to a tight (upper and lower bound) characterization of the communication complexity of the set intersection problem on subsets of {1,...,n} (the player are required to compute the intersection of their sets), whose randomized communication complexity tends to C∧⋅ n pm o(n) as the error tends to zero.