We show that a randomly chosen linear map over a finite field gives a good hash function in the $\ell_{\infty}$ sense. More concretely, consider a set $S\subset\mathbb{F}_{q}^{n}$ and a randomly chosen linear ${map}L:\mathbb{F}_{q}^{n}\rightarrow\mathbb{F}_{q}^{t}$ with qttaken to be sufficiently smaller than $|S|$. Let USdenote a random variable distributed uniformly on S. Our main theorem shows that, with high probability over the choice of L, the random variable $L(U_{S})$ is close to uniform in the $\ell_{\infty}$ norm. In other words, every element in the range $\mathbb{F}_{q}^{t}$ has about the same number of elements in S mapped to it. This complements the widely-used Leftover Hash Lemma (LHL) which proves the analog statement under the statistical, or $\ell_{1}$, distance (for a richer class of functions) as well as prior work on the expected largest ’bucket size’ in linear hash functions [1]. By known bounds from the load balancing literature [2], our results are tight and show that linear functions hash as well as truly random function up to a constant factor in the entropy loss. Our proof leverages a connection between linear hashing and the finite field Kakeya problem and extends some of the tools developed in this area, in particular the polynomial method.

We introduce a simple logical inference structure we call a “spanoid" (generalizing the notion of a matroid), which captures well-studied problems in several areas. These include combinatorial geometry (point-line incidences), algebra (arrangements of hypersurfaces and ideals), statistical physics (bootstrap percolation), network theory (gossip/infection processes) and coding theory. We initiate a thorough investigation of spanoids, from computational and structural viewpoints, focusing on parameters relevant to the applications areas above and, in particular, to questions regarding locally correctable codes (LCCs). One central parameter we study is the “rank" of a spanoid, extending the rank of a matroid and related to the dimension of codes. This leads to one main application of our work, establishing the first known barrier to improving the nearly 20-year old bound of Katz--Trevisan (KT) on the dimension of LCCs. On the one hand, we prove that the KT bound (and its more recent refinements) holds for the much more general setting of spanoid rank. On the other hand we show that there exist (random) spanoids whose rank matches these bounds. Thus, to significantly improve the known bounds one must step out of the spanoid framework. Another parameter we explore is the “functional rank" of a spanoid, which captures the possibility of turning a given spanoid into an actual code. The question of the relationship between rank and functional rank is one of the main questions we raise as it may reveal new avenues for constructing new LCCs (perhaps even matching the KT bound). As a first step, we develop an entropy relaxation of functional rank to create a small constant gap and amplify it by tensoring to construct a spanoid whose functional rank is smaller than rank by a polynomial factor. This is evidence that the entropy method we develop can prove polynomially better bounds than KT-type methods on the dimension of LCCs. To facilitate the above results we also develop some basic structural results on spanoids including an equivalent formulation of spanoids as set systems and properties of spanoid products. We feel that given these initial findings and their motivations, the abstract study of spanoids merits further investigation. We leave plenty of concrete open problems and directions.

The concept of matrix rigidity was first introduced by Valiant in [Friedman, 1993]. Roughly speaking, a matrix is rigid if its rank cannot be reduced significantly by changing a small number of entries. There has been extensive interest in rigid matrices as Valiant showed in [Friedman, 1993] that rigidity can be used to prove arithmetic circuit lower bounds. In a surprising result, Alman and Williams showed that the (real valued) Hadamard matrix, which was conjectured to be rigid, is actually not very rigid. This line of work was extended by [Dvir and Edelman, 2017] to a family of matrices related to the Hadamard matrix, but over finite fields. In our work, we take another step in this direction and show that for any abelian group G and function f:G - > {C}, the matrix given by M_{xy} = f(x - y) for x,y in G is not rigid. In particular, we get that complex valued Fourier matrices, circulant matrices, and Toeplitz matrices are all not rigid and cannot be used to carry out Valiant’s approach to proving circuit lower bounds. This complements a recent result of Goldreich and Tal [Goldreich and Tal, 2016] who showed that Toeplitz matrices are nontrivially rigid (but not enough for Valiant’s method). Our work differs from previous non-rigidity results in that those works considered matrices whose underlying group of symmetries was of the form {F}_p^n with p fixed and n tending to infinity, while in the families of matrices we study, the underlying group of symmetries can be any abelian group and, in particular, the cyclic group {Z}_N, which has very different structure. Our results also suggest natural new candidates for rigidity in the form of matrices whose symmetry groups are highly non-abelian. Our proof has four parts. The first extends the results of [Josh Alman and Ryan Williams, 2016; Dvir and Edelman, 2017] to generalized Hadamard matrices over the complex numbers via a new proof technique. The second part handles the N x N Fourier matrix when N has a particularly nice factorization that allows us to embed smaller copies of (generalized) Hadamard matrices inside of it. The third part uses results from number theory to bootstrap the non-rigidity for these special values of N and extend to all sufficiently large N. The fourth and final part involves using the non-rigidity of the Fourier matrix to show that the group algebra matrix, given by M_{xy} = f(x - y) for x,y in G, is not rigid for any function f and abelian group G.

We introduce a simple logical inference structure we call a spanoid (generalizing the notion of a matroid), which captures well-studied problems in several areas. These include combinatorial geometry (point-line incidences), algebra (arrangements of hypersurfaces and ideals), statistical physics (bootstrap percolation), network theory (gossip / infection processes) and coding theory. We initiate a thorough investigation of spanoids, from computational and structural viewpoints, focusing on parameters relevant to the applications areas above and, in particular, to questions regarding Locally Correctable Codes (LCCs). One central parameter we study is the rank of a spanoid, extending the rank of a matroid and related to the dimension of codes. This leads to one main application of our work, establishing the first known barrier to improving the nearly 20-year old bound of Katz-Trevisan (KT) on the dimension of LCCs. On the one hand, we prove that the KT bound (and its more recent refinements) holds for the much more general setting of spanoid rank. On the other hand we show that there exist (random) spanoids whose rank matches these bounds. Thus, to significantly improve the known bounds one must step out of the spanoid framework. Another parameter we explore is the functional rank of a spanoid, which captures the possibility of turning a given spanoid into an actual code. The question of the relationship between rank and functional rank is one of the main questions we raise as it may reveal new avenues for constructing new LCCs (perhaps even matching the KT bound). As a first step, we develop an entropy relaxation of functional rank to create a small constant gap and amplify it by tensoring to construct a spanoid whose functional rank is smaller than rank by a polynomial factor. This is evidence that the entropy method we develop can prove polynomially better bounds than KT-type methods on the dimension of LCCs. To facilitate the above results we also develop some basic structural results on spanoids including an equivalent formulation of spanoids as set systems and properties of spanoid products. We feel that given these initial findings and their motivations, the abstract study of spanoids merits further investigation. We leave plenty of concrete open problems and directions.

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.

Kelly's theorem states that a set of n points affinely spanning C^3 must determine at least one ordinary complex line (a line passing through exactly two of the points). Our main theorem shows that such sets determine at least 3n/2 ordinary lines, unless the configuration has n-1 points in a plane and one point outside the plane (in which case there are at least n-1 ordinary lines). In addition, when at most n/2 points are contained in any plane, we prove a theorem giving stronger bounds that take advantage of the existence of lines with four and more points (in the spirit of Melchior's and Hirzebruch's inequalities). Furthermore, when the points span four or more dimensions, with at most n/2 points contained in any three dimensional affine subspace, we show that there must be a quadratic number of ordinary lines.

Locally decodable codes (LDCs) are error correcting codes that allow for decoding of a single message bit using a small number of queries to a corrupted encoding. Despite decades of study, the optimal trade-off between query complexity and codeword length is far from understood. In this work, we give a new characterization of LDCs using distributions over Boolean functions whose expectation is hard to approximate (in~$L_\infty$~norm) with a small number of samples. We coin the term `outlaw distributions' for such distributions since they `defy' the Law of Large Numbers. We show that the existence of outlaw distributions over sufficiently `smooth' functions implies the existence of constant query LDCs and vice versa. We give several candidates for outlaw distributions over smooth functions coming from finite field incidence geometry, additive combinatorics and from hypergraph (non)expanders. We also prove a useful lemma showing that (smooth) LDCs which are only required to work on average over a random message and a random message index can be turned into true LDCs at the cost of only constant factors in the parameters.

A 2-server Private Information Retrieval (PIR) scheme allows a user to retrieve the i th bit of an n -bit database replicated among two noncommunicating servers, while not revealing any information about i to either server. In this work, we construct a 2-server PIR scheme with total communication cost n O (√log / log n log n ). This improves over current 2-server protocols, which all require Ω( n 1/3 ) communication. Our construction circumvents the n 1/3 barrier of Razborov and Yekhanin [2007], which holds for the restricted model of bilinear group-based schemes (covering all previous 2-server schemes). The improvement comes from reducing the number of servers in existing protocols, based on Matching Vector Codes, from 3 or 4 servers to 2. This is achieved by viewing these protocols in an algebraic way (using polynomial interpolation) and extending them using partial derivatives.

We prove a new upper bound on the number of $r$-rich lines (lines with at least $r$ points) in a `truly' $d$-dimensional configuration of points $v_1,\ldots,v_n \in \mathbb{C}^d$. More formally, we show that, if the number of $r$-rich lines is significantly larger than $n^2/r^d$ then there must exist a large subset of the points contained in a hyperplane. We conjecture that the factor $r^d$ can be replaced with a tight $r^{d+1}$. If true, this would generalize the classic Szemerédi-Trotter theorem which gives a bound of $n^2/r^3$ on the number of $r$-rich lines in a planar configuration. This conjecture was shown to hold in $\mathbb{R}^3$ in the seminal work of Guth and Katz \cite{GK10} and was also recently proved over $\mathbb{R}^4$ (under some additional restrictions) \cite{SS14}. For the special case of arithmetic progressions ($r$ collinear points that are evenly distanced) we give a bound that is tight up to low order terms, showing that a $d$-dimensional grid achieves the largest number of $r$-term progressions. The main ingredient in the proof is a new method to find a low degree polynomial that vanishes on many of the rich lines. Unlike previous applications of the polynomial method, we do not find this polynomial by interpolation. The starting observation is that the degree $r-2$ Veronese embedding takes $r$-collinear points to $r$ linearly dependent images. Hence, each collinear $r$-tuple of points, gives us a dependent $r$-tuple of images. We then use the design-matrix method of \cite{BDWY12} to convert these 'local' linear dependencies into a global one, showing that all the images lie in a hyperplane. This then translates into a low degree polynomial vanishing on the original set.

In this work we study arrangements of k-dimensional subspaces V_1,...,V_n over the complex numbers. Our main result shows that, if every pair V_a, V_b of subspaces is contained in a dependent triple (a triple V_a, V_b, V_c contained in a 2k-dimensional space), then the entire arrangement must be contained in a subspace whose dimension depends only on k (and not on n). The theorem holds under the assumption that the subspaces are pairwise non-intersecting (otherwise it is false). This generalizes the Sylvester-Gallai theorem (or Kelly's theorem for complex numbers), which proves the k=1 case. Our proof also handles arrangements in which we have many pairs (instead of all) appearing in dependent triples, generalizing the quantitative results of Barak et. al. One of the main ingredients in the proof is a strengthening of a theorem of Barthe (from the k=1 to k>1 case) proving the existence of a linear map that makes the angles between pairs of subspaces large on average. Such a mapping can be found, unless there is an obstruction in the form of a low dimensional subspace intersecting many of the spaces in the arrangement (in which case one can use a different argument to prove the main theorem).

A 2-server Private Information Retrieval (PIR) scheme allows a user to retrieve the ith bit of an n-bit database replicated among two non-communicating servers, while not revealing any information about i to either server. In this work we construct a 2-server PIR scheme with total communication cost nO√(log log n)/(log n). This improves over current 2-server protocols which all require Ω(n1/3) communication. Our construction circumvents the n1/3 barrier of Razborov and Yekhanin which holds for the restricted model of bilinear group-based schemes (covering all previous 2-server schemes). The improvement comes from reducing the number of servers in existing protocols, based on Matching Vector Codes, from 3 or 4 servers to 2. This is achieved by viewing these protocols in an algebraic way (using polynomial interpolation) and extending them using partial derivatives.

We prove that 3-query linear locally correctable codes over the Reals of dimension d require block length n > d2+λ for some fixed, positive λ > 0. Geometrically, this means that if n vectors in Rd are such that each vector is spanned by a linear number of disjoint triples of others, then it must be that n > d2+λ. This improves the known quadratic lower bounds (e.g. [20, 28]). While a modest improvement, we expect that the new techniques introduced in this work will be useful for further progress on lower bounds of locally correctable and decodable codes with more than 2 queries.

A matching vector (MV) family modulo $m$ is a pair of ordered lists $U=(u_1,\ldots,u_t)$ and $V=(v_1,\ldots,v_t)$ where $u_i$, $v_j \in \mathbb{Z}_m^n$ with the following inner product pattern: for any $i$, $\langle u_i,v_i\rangle=0$, and for any $i \ne j$, $\langle u_i,v_j\rangle \ne 0$. An MV family is called $q$-restricted if inner products $\langle u_i,v_j\rangle$ take at most $q$ different values. Our interest in MV families stems from their recent application in the construction of subexponential locally decodable codes (LDCs). There, $q$-restricted MV families are used to construct LDCs with $q$ queries, and there is special interest in the regime where $q$ is constant. When $m$ is a prime it is known that such constructions yield codes with exponential block length. However, for composite $m$ the behavior is dramatically different. A recent work by Efremenko [SIAM J. Comput., 40 (2011), pp. 1154--1178] (based on an approach initiated by Yekhanin [J. ACM, 55 (2008), pp. 1--16]) gives the first subexponential LDC with constant queries. It is based on a construction of an MV family of superpolynomial size by Grolmusz [Combinatorica, 20 (2000), pp. 71--86] modulo composite $m$. In this work, we prove two lower bounds on the block length of LDCs which are based on black box construction using MV families. When $q$ is constant (or sufficiently small), we prove that such LDCs must have a quadratic block length. When the modulus $m$ is constant (as it is in the construction of Efremenko) we prove a superpolynomial lower bound on the block-length of the LDCs, assuming a well-known conjecture in additive combinatorics, the polynomial Freiman--Ruzsa conjecture over $\mathbb{Z}_m$.

A Matching Vector (MV) family modulo m is a pair of ordered lists U=(u1,...,ut) and V=(v1,...,vt) where ui,vj ∈ Zmn with the following inner product pattern: for any i, {ui,vi}=0, and for any i ≠ j, {ui,vj} ≠ 0. A MV family is called q-restricted if inner products {ui,vj} take at most q different values.

We extend the “method of multiplicities” to get the following results, of interest in combinatorics and randomness extraction. (i) We show that every Kakeya set in $\mathbb{F}_q^n$, the $n$-dimensional vector space over the finite field on $q$ elements, must be of size at least $q^n/2^n$. This bound is tight to within a $2+o(1)$ factor for every $n$ as $q\to\infty$. (ii) We give improved “randomness mergers”: Mergers are seeded functions that take as input $\ell$ (possibly correlated) random variables in $\{0,1\}^N$ and a short random seed and output a single random variable in $\{0,1\}^N$ that is statistically close to having entropy $(1-\delta)\cdot N$ when one of the $\ell$ input variables is distributed uniformly. The seed we require is only $(1/\delta)\cdot\log\ell$-bits long, which significantly improves upon previous construction of mergers. (iii) We give improved randomness extractors, based on our improved mergers. Specifically, we show how to construct randomness extractors that use logarithmic length seeds while extracting $1-o(1)$ fraction of the min-entropy of the source. Previous results could extract only a constant fraction of the entropy while maintaining logarithmic seed length. The “method of multiplicitie” was used in prior work to analyze combinatorial parameters of “algebraically nice” subsets of vector spaces over finite fields. The method works by constructing somewhat low-degree interpolating polynomials that vanish on every point in the subset with high multiplicity. The typical use of this method involves using the “algebraic niceness” to show that the interpolating polynomial also vanishes on some points outside the subset. It then uses simple bounds on the number of zeroes of low-degree polynomials to bound the combinatorial parameter of interest. Our augmentation to this technique is that we prove, under appropriate conditions, that the interpolating polynomial vanishes with high multiplicity outside the set. This novelty leads to significantly tighter analyses. To develop the extended method of multiplicities, we provide a number of basic technical results about multiplicity of zeroes of polynomials that may be of general use. For instance, we strengthen the Schwartz--Zippel lemma to show that the expected multiplicity of zeroes of a nonzero degree $d$ polynomial at a random point in $S^n$, for any finite subset $S$ of the underlying field, is at most $d/|S|$ (a fact that does not seem to have been noticed in the CS literature before).

We introduce a notion of non-black-box access to computational devices (such as circuits, formulas, decision trees, and so forth) that we call restriction access. Restrictions are partial assignments to input variables. Each restriction simplifies the device, and yields a new device for the restricted function on the unassigned variables. On one extreme, full restrictions (assigning all variables) correspond to evaluating the device on a complete input, yielding the result of the computation on that input, which is the same as standard black-box access. On the other extreme, empty restrictions (assigning no variables) yield a full description of the original device. We explore the grey-scale of possibilities in the middle.

We construct explicit subspace-evasive sets. These are subsets of Fn of size |F|(1-ε)n whose intersection with any k-dimensional subspace is bounded by a constant c(k,ε). This problem was raised by Guruswami (CCC 2011) as it leads to optimal rate list-decodable codes of constant list size. The main technical ingredient is the construction of k low-degree polynomials whose common set of zeros has small intersection with any k-dimensional subspace.

This work deals with the power of linear algebra in the context of multilinear computation. By linear algebra we mean algebraic branching programs (ABPs) which are known to be computationally equivalent to two basic tools in linear algebra: iterated matrix multiplication and the determinant. We compare the computational power of multilinear ABPs to that of multilinear arithmetic formulas, and prove a tight super-polynomial separation between the two models. Specifically, we describe an explicit n-variate polynomial F that is computed by a linear-size multilinear ABP but every multilinear formula computing F must be of size nΩ(log n).

A Locally Correctable Code (LCC) is an error correcting code that has a probabilistic self-correcting algorithm that, with high probability, can correct any coordinate of the codeword by looking at only a few other coordinates, even if a fraction δ of the coordinates are corrupted. LCCs are a stronger form of LDCs (Locally Decodable Codes) which have received a lot of attention recently due to their many applications and surprising constructions. In this work we show a separation between 2-query LDCs and LCCs over finite fields of prime order. Specifically, we prove a lower bound of the form p^{Ω(δd)} on the length of linear 2-query LCCs over $\F_p$, that encode messages of length d. Our bound improves over the known bound of $2^{Ω(δd)} \cite{GKST06, KdW04, DS07} which is tight for LDCs. Our proof makes use of tools from additive combinatorics which have played an important role in several recent results in theoretical computer science. Corollaries of our main theorem are new incidence geometry results over finite fields. The first is an improvement to the Sylvester-Gallai theorem over finite fields \cite{SS10} and the second is a new analog of Beck's theorem over finite fields.

A (q,k,t)-design matrix is an m x n matrix whose pattern of zeros/non-zeros satisfies the following design-like condition: each row has at most q non-zeros, each column has at least k non-zeros and the supports of every two columns intersect in at most t rows. We prove that for m ≥ n, the rank of any (q,k,t)-design matrix over a field of characteristic zero (or sufficiently large finite characteristic) is at least n - (qtn/2k)2. Using this result we derive the following applications: Impossibility results for 2-query LCCs over large fields: A 2-query locally correctable code (LCC) is an error correcting code in which every codeword coordinate can be recovered, probabilistically, by reading at most two other code positions. Such codes have numerous applications and constructions (with exponential encoding length) are known over finite fields of small characteristic. We show that infinite families of such linear 2-query LCCs do not exist over fields of characteristic zero or large characteristic regardless of the encoding length. Generalization of known results in combinatorial geometry: We prove a quantitative analog of the Sylvester-Gallai theorem: Let v1,...,vm be a set of points in Cd such that for every i ∈ [m] there exists at least δ m values of j ∈ [m] such that the line through vi,vj contains a third point in the set. We show that the dimension of v1,...,vm is at most O(1/δ2). Our results generalize to the high-dimensional case (replaceing lines with planes, etc.) and to the case where the points are colored (as in the Motzkin-Rabin Theorem).

We describe a new approach for the problem of finding rigid matrices, as posed by Valiant [Val77], by connecting it to the, seemingly unrelated, problem of proving lower bounds for linear locally self-correctable codes. This approach, if successful, could lead to a non-natural property (in the sense of Razborov and Rudich [RR97]) implying super-linear lower bounds for linear functions in the model of logarithmic-depth arithmetic circuits. Our results are based on a lemma saying that, if the generating matrix of a locally decodable code is not rigid, then it defines a locally self-correctable code with rate close to one. Thus, showing that such codes cannot exist will prove that the generating matrix of any locally decodable code (and in particular Reed Muller codes) is rigid.

An $(r,\delta,\epsilon)$-locally decodable code encodes a k-bit message x to an N-bit codeword $C(x)$, such that for every $i\in[k]$, the ith message bit can be recovered with probability $1-\epsilon$, by a randomized decoding procedure that queries only r bits, even if the codeword $C(x)$ is corrupted in up to $\delta N$ locations. Recently a new class of locally decodable codes (LDCs), based on families of vectors with restricted dot products, has been discovered. We refer to those codes as matching vector (MV) codes. Several families of $(r,\delta,\Theta(r\delta))$-locally decodable MV codes have been obtained. While codes in those families were shorter than codes of earlier generations, they suffered from having large values of $\epsilon=\Omega(r\delta)$, which meant that r-query MV codes could only handle error rates below $\frac{1}{r}$. Thus larger query complexity gave shorter length codes but at the price of less error tolerance. No MV codes of a superconstant number of queries capable of tolerating a constant fraction of errors were known to exist. In this paper we present a new view of matching vector codes and uncover certain similarities between MV codes and classical Reed–Muller (RM) codes. Our view allows us to obtain deeper insights into the power and limitations of MV codes. Specifically, we obtain the following: (1) We show that existing families of MV codes can be enhanced to tolerate a large constant fraction of errors, independent of the number of queries. Such enhancement comes at a price of a moderate increase in the number of queries. (2) Our construction yields the first families of MV codes of superconstant query complexity that can tolerate a constant fraction of errors. Our codes are shorter than RM LDCs for all values of $r\leq\log k/(\log\log k)^c$, for some constant c. (3) We show that any MV code encodes messages of length k to codewords of length at least $k2^{\Omega(\sqrt{\log k})}$. Therefore MV codes do not improve upon RM LDCs for $r\geq(\log k)^{\Omega(\sqrt{\log k})}$.

A locally decodable code encodes a message by a codeword, such that even if the codeword is corrupted by noise, each message bit can be recovered with high probability by a randomized decoding procedure that reads only few bits of the codeword. Recently a new class of locally decodable codes, based on families of vectors with restricted dot products has been discovered. We refer to those codes as Matching Vector (MV) codes. In this work we develop a new view of MV codes and uncover certain similarities between them and classical Reed Muller codes. Our view allows us to obtain a deeper insight into the power and limitations of MV codes. We use it to construct codes that can tolerate more errors or are shorter than previously known codes for certain parameter settings. We also show super-linear lower bounds on the codeword length of any MV code.

We study the task of randomness extraction from sources which are distributed uniformly on an unknown algebraic variety. In other words, we are interested in constructing a function (an extractor) whose output is close to uniform even if the input is drawn uniformly from the set of solutions of an unknown system of low degree polynomials. This problem generalizes the problem of extraction from affine sources which has drawn a considerable amount of interest lately. We present two constructions of explicit extractors for varieties. The first works for varieties of any size (including one dimensional varieties, or curves) and requires field size which is exponential in the overall dimension of the space. Our second extractor allows the field size to be polynomial in the degree of the equations defining the variety, but works only for varieties whose size is at least the square root of the total size of the space.

We extend the "method of multiplicities" to get the following results, of interest in combinatorics and randomness extraction. 1) We show that every Kakeya set (a set of points that contains a line in every direction) in Fqnmust be of size at least qn/2n. This bound is tight to within a 2 + o(1) factor for every n as q ? ?, compared to previous bounds that were off by exponential factors in n. 2) We give an improved construction of "randomness mergers". Mergers are seeded functions that take as input ? (possibly correlated) random variables in {0,1}Nand a short random seed, and output a single random variable in {0,1}Nthat is statistically close to having entropy (1 - ?) ? N when one of the ? input variables is distributed uniformly. The seed we require is only (1/?) ? log ?-bits long, which significantly improves upon previous construction of mergers. 3) We show how to construct randomness extractors that use logarithmic length seeds while extracting 1 - o(1) fraction of the min-entropy of the source. Previous results could extract only a constant fraction of the entropy while maintaining logarithmic seed length. The "method of multiplicities", as used in prior work, analyzed subsets of vector spaces over finite fields by constructing somewhat low degree interpolating polynomials that vanish on every point in the subset with high multiplicity. The typical use of this method involved showing that the interpolating polynomial also vanished on some points outside the subset, and then used simple bounds on the number of zeroes to complete the analysis. Our augmentation to this technique is that we prove, under appropriate conditions, that the interpolating polynomial vanishes with high multiplicity outside the set. This novelty leads to significantly tighter analyses. To develop the extended method of multiplicities we provide a number of basic technical results about multiplicity of zeroes of polynomials that may be of general use. For instance, we strengthen the Schwartz-Zippel lemma to show that the expected multiplicity of zeroes of a non-zero degree d polynomial at a random point in Sn, for any finite subset S of the underlying field, is at most d/|S|.

In this paper we show that lower bounds for bounded depth arithmetic circuits imply derandomization of polynomial identity testing for bounded depth arithmetic circuits. More formally, if there exists an explicit polynomial f that cannot be computed by a depth d arithmetic circuit of small size, then there exists an efficient deterministic black-box algorithm to test whether a given depth $d-5$ circuit that computes a polynomial of relatively small individual degrees is identically zero or not. In particular, if we are guaranteed that the tested circuit computes a multilinear polynomial, then we can perform the identity test efficiently. To the best of our knowledge this is the first hardness-randomness tradeoff for bounded depth arithmetic circuits. The above results are obtained using the arithmetic Nisan–Wigderson generator of Kabanets and Impagliazzo together with a new theorem on bounded depth circuits, which is the main technical contribution of our work. This theorem deals with polynomial equations of the form $P(x_1,\dots,x_n,y)\equiv0$ and shows that if P has a circuit of depth d and size s and if the polynomial $f(x_1,\dots,x_n)$ satisfies $P(x_1,\dots,x_n,f)\equiv0$, then f has a circuit of depth $d+3$ and size $\mathrm{poly}(s,m^r)$, where m is the total degree of f and r is the degree of y in P. This circuit for f can be found probabilistically in time $\mathrm{poly}(s,m^r)$. In the other direction we observe that the methods of Kabanets and Impagliazzo can be used to show that derandomizing identity testing for bounded depth circuits implies lower bounds for the same class of circuits. More formally, if we can derandomize polynomial identity testing for bounded depth circuits, then NEXP does not have bounded depth arithmetic circuits. That is, either $\mathrm{NEXP}\not\subseteq P/\mathrm{poly}$ or the Permanent is not computable by polynomial size bounded depth arithmetic circuits.

A noisy interpolating set (NIS) for degree d polynomials is a set S sube Fn, where F is a finite field, such that any degree d polynomial q isin F[x1,..., xn] can be efficiently interpolated from its values on S, even if an adversary corrupts a constant fraction of the values. In this paper we construct explicit NIS for every prime field Fpand any degree d. Our sets are of size O(nd) and have efficient interpolation algorithms that can recover qfrom a fraction exp(-O(d)) of errors. Our construction is based on a theorem which roughly states that ifS is a NIS for degree I polynomials then dldrS = {alpha1+ ... + alphad| alpha1isin S} is a NIS for degree d polynomials. Furthermore, given an efficient interpolation algorithm for S, we show how to use it in a black-box manner to build an efficient interpolation algorithm for d ldr S. As a corollary we get an explicit family of punctured Reed-Muller codes that is a family of good codes that have an efficient decoding algorithm from a constant fraction of errors. To the best of our knowledge no such construction was known previously.

A merger is a probabilistic procedure which extracts the randomness out of any (arbitrarily correlated) set of random variables, as long as one of them is uniform. Our main result is an efficient, simple, optimal (to constant factors) merger, which, for k random vairables on n bits each, uses a O(log(nk)) seed, and whose error is 1/nk. Our merger can be viewed as a derandomized version of the merger of Lu, Reingold, Vadhan and Wigderson (2003). Its analysis generalizes the recent resolutionof the Kakeya problem in finite fields of Dvir (2008). Following the plan set forth by Ta-Shma (1996), who defined mergers as part of this plan, our merger provides the last "missing link" to a simple and modular construction of extractors for all entropies, which is optimal to constant factorsin all parameters. This complements the elegant construction of optimal extractor by Guruswami, Vadhan and Umans (2007). We also give simple extensions of our merger in two directions. First, we generalize it to handle the case where no source is uniform - in that case the merger will extract the entropy present in the most random of the given sources. Second, we observe that the merger works just as well in the computational setting, when the sources are efficiently samplable, and computational notions of entropy replace the information theoretic ones.

In this paper we construct explicit deterministic extractors from polynomial sources, namely from distributions sampled by low degree multivariate polynomials over finite fields. This naturally generalizes previous work on extraction from affine sources. A direct consequence is a deterministic extractor for distributions sampled by polynomial size arithmetic circuits over exponentially large fields. The first step towards extraction is a construction o/rank extractors, which are polynomial mappings that "extract" the algebraic rank from any system of low degree polynomials. More precisely, for any n polynomials, k of which are algebraically independent, a rank extractor outputs k algebraically independent polynomials of slightly higher degree. A result of Wooley allows us to relate algebraic rank and min-entropy and to show that a rank extractor is also a high quality condenser for polynomial sources over polynomially large fields. Finally, to turn this condenser into an extractor, we employ a theorem of Bombieri, giving a character sum estimate for polynomials defined over curves. It allows extracting all the randomness (up to a multiplicative constant) from polynomial sources over exponentially large fields.

In this work we study two, seemingly unrelated, notions. Locally decodable codes (LDCs) are codes that allow the recovery of each message bit from a constant number of entries of the codeword. Polynomial identity testing (PIT) is one of the fundamental problems of algebraic complexity: we are given a circuit computing a multivariate polynomial and we have to determine whether the polynomial is identically zero. We improve known results on LDCs and on polynomial identity testing and show a relation between the two notions. In particular we obtain the following results: (1) We show that if $E: \mathbb{F}^n \mapsto \mathbb{F}^m$ is a linear LDC with two queries, then $m = \exp(\Omega(n))$. Previously this was known only for fields of size $\ll 2^n$ [O. Goldreich et al., Comput. Complexity, 15 (2006), pp. 263–296]. (2) We show that from every depth 3 arithmetic circuit ($\Sigma\Pi\Sigma$ circuit), ${\cal C}$, with a bounded (constant) top fan‐in that computes the zero polynomial, one can construct an LDC. More formally, assume that ${\cal C}$ is minimal (no subset of the multiplication gates sums to zero) and simple (no linear function appears in all the multiplication gates). Denote by d the degree of the polynomial computed by ${\cal C}$ and by r the rank of the linear functions appearing in ${\cal C}$. Then we can construct a linear LDC with two queries that encodes messages of length $r/{\operatorname{polylog}(d)}$ by codewords of length $O(d)$. (3) We prove a structural theorem for $\Sigma\Pi\Sigma$ circuits, with a bounded top fan‐in, that compute the zero polynomial. In particular we show that if such a circuit is simple, minimal, and of polynomial size, then its rank, r, is only polylogarithmic in the number of variables (a priori it could have been linear). (4) We give new PIT algorithms for $\Sigma\Pi\Sigma$ circuits with a bounded top fan‐in: (a) a deterministic algorithm that runs in quasipolynomial time, and (b) a randomized algorithm that runs in polynomial time and uses only a polylogarithmic number of random bits. Moreover, when the circuit is multilinear, our deterministic algorithm runs in polynomial time. Previously deterministic subexponential time algorithms for PIT in bounded depth circuits were known only for depth 2 circuits (in the black box model) [D. Grigoriev, M. Karpinski, and M. F. Singer, SIAM J. Comput., 19 (1990), pp. 1059–1063; M. Ben‐Or and P. Tiwari, Proceedings of the 20th Annual ACM Symposium on Theory of Computing, ACM Press, New York, 1988, pp. 301–309; A. R. Klivans and D. Spielman, Proceedings of the 33rd Annual ACM Symposium on Theory of Computing, ACM Press, New York, 2001, pp. 216–223]. In particular, for the special case of depth 3 circuits with three multiplication gates our result resolves an open question asked by Klivans and Spielman.

In this work we study two, seemingly unrelated, notions. Locally Decodable Codes (LDCs) are codes that allow the recovery of each message bit from a constant number of entries of the codeword. Polynomial Identity Testing (PIT) is one of the fundamental problems of algebraic complexity: we are given a circuit computing a multivariate polynomial and we have to determine whether the polynomial is identically zero. We improve known results on locally decodable codes and on polynomial identity testing and show a relation between the two notions. In particular we obtain the following results: