ϵ-Minimum Storage Regenerating (E-MSR) codes form a special class of Maximum Distance Separable (MDS) codes, providing mechanisms for exact regeneration of a single code block in their codewords by downloading slightly suboptimal amount of information from the remaining code blocks. The key advantage of these codes is a significantly lower subpacketization that grows only logarithmically with the code length, while providing optimality in storage and error-correcting capacity. However, existing constructions of ϵ-MSR codes require each remaining code block to be available for the repair of any failed code block. In this paper, we construct ϵ-MSR codes that can repair any failed code block by contacting fewer number of available code blocks. When a code block fails, our repair procedure needs to contact a few compulsory code blocks and is free to choose any subset of available code blocks for the remaining choices. Our construction requires a field size linear in code length and ensures load balancing (in terms of information downloaded) among the contacted code blocks for repairing a failed code block.

ε-Minimum Storage Regenerating (ε -MSR) codes form a special class of Maximum Distance Separable (MDS) codes, providing mechanisms for exact regeneration of a single code block in their codewords by downloading slightly suboptimal amount of information from the remaining code blocks. The key advantage of these codes is a significantly lower sub-packetization that grows only logarithmically with the length of the code, while providing optimality in storage and error-correcting capacity. However, from an implementation point of view, these codes require each remaining code block to be available for the repair of any single code block. In this paper, we address this issue by constructing ε -MSR codes that can repair a failed code block by contacting a fewer number of available code blocks. When a code block fails, our repair procedure needs to contact a few compulsory code blocks and is free to choose any subset of a fixed size for the remaining choices (from the available code blocks). Further, our construction requires a field size linear in code length and ensures load balancing among the contacted code blocks in terms of information downloaded from them.

Various combinatorial/algebraic parameters are used to quantify the complexity of a Boolean function. Among them, sensitivity is one of the simplest and block sensitivity is one of the most useful. Nisan (1989) and Nisan and Szegedy (1991) showed that block sensitivity and several other parameters, such as certificate complexity, decision tree depth, and degree over R, are all polynomially related to one another. The sensitivity conjecture states that there is also a polynomial relationship between sensitivity and block sensitivity, thus supplying the "missing link". Since its introduction in 1991, the sensitivity conjecture has remained a challenging open question in the study of Boolean functions. One natural approach is to prove it for special classes of functions. For instance, the conjecture is known to be true for monotone functions, symmetric functions, and functions describing graph properties. In this paper, we consider the conjecture for Boolean functions computable by read-k formulas. A read-k formula is a tree in which each variable appears at most k times among the leaves and has Boolean gates at its internal nodes. We show that the sensitivity conjecture holds for read-once formulas with gates computing symmetric functions. We next consider regular formulas with OR and AND gates. A formula is regular if it is a leveled tree with all gates at a given level having the same fan-in and computing the same function. We prove the sensitivity conjecture for constant depth regular read-k formulas for constant k.

A Boolean function f : {0, 1}n→ {0, 1} is called nondegenerate if f depends on all its n variables. We show that, for any nondegenerate function f, there exists a variable xisuch that at least one of the restrictions fIxi=0or fIxi=1must depend on all the remaining n - 1 variables. We also consider lower bounds on the degrees of polynomials representing a Boolean function over different rings. Let dq(f) be the degree of the (unique) polynomial over the ring ℤqexactly representing f. For distinct primes pilet m = Πri=1peii. Then, we show that any nondegenerate symmetric Boolean function f must have m · dp1e1(f)...dprer(f) > n. We use the existence of nondegenerate subfunctions to prove degree lower bounds on random functions. Specifically, we show that m · dp1e1(f)...dprer(f) > lg n - 1 holds for almost all f when f is chosen uniformly at random from all n-variate Boolean functions. Our proof uses the second moment method to show that a random f must almost always contain a nondegenerate symmetric subfunction on at least lg n - 1 variables. It follows that an n-variate nondegenerate symmetric Boolean function can have degree o(√(n)) over at most one finite field and that almost all f can have degree o(√(lg n)) over at most one finite field.

We present a randomized algorithm for reconstructing multilinear ΣΠΣΠ(2) circuits, i.e., multilinear depth-4 circuits with fan-in 2 at the top + gate. The algorithm is given blackbox access to a polynomial f ∈ F[x1,...,xn] computable by a multilinear ΣΠΣΠ(2) circuit of size s and outputs an equivalent multilinear ΣΠΣΠ(2) circuit, runs in time poly(n,s), and works over any field F. This is the first reconstruction result for any model of depth-4 arithmetic circuits. Prior to our work, reconstruction results for bounded depth circuits were known only for depth-2 arithmetic circuits (Klivans & Spielman, STOC 2001), ΣΠΣ(2) circuits (depth-3 arithmetic circuits with top fan-in 2) (Shpilka, STOC 2007), and ΣΠΣ(k) with k=O(1) (Karnin & Shpilka, CCC 2009). Moreover, the running times of these algorithms have a polynomial dependence on |F| and hence do not work for infinite fields such as Q.

In the reconstruction problem for a multivariate polynomial f, we have black box access to f and the goal is to efficiently reconstruct a representation of f in a suitable model of computation. We give a polynomial time randomized algorithm for reconstructing random multilinear formulas. Our algorithm succeeds with high probability when given black box access to the polynomial computed by a random multilinear formula according to a natural distribution. This is the strongest model of computation for which a reconstruction algorithm is presently known, albeit efficient in a distributional sense rather than in the worst-case. Previous results on this problem considered much weaker models such as depth-3 circuits with various restrictions or read-once formulas. Our proof uses ranks of partial derivative matrices as a key ingredient and combines it with analysis of the algebraic structure of random multilinear formulas. Partial derivative matrices have earlier been used to prove lower bounds in a number of models of arithmetic complexity, including multilinear formulas and constant depth circuits. As such, our results give supporting evidence to the general thesis that mathematical properties that capture efficient computation in a model should also enable learning algorithms for functions efficiently computable in that model.

The rigidity of a matrix $A$ for target rank $r$ is the minimum number of entries of $A$ that must be changed to ensure that the rank of the altered matrix is at most $r$. Since its introduction by Valiant \cite{Val77}, rigidity and similar rank-robustness functions of matrices have found numerous applications in circuit complexity, communication complexity, and learning complexity. Almost all $\nbyn$ matrices over an infinite field have a rigidity of $(n-r)^2$. It is a long-standing open question to construct infinite families of \emph{explicit} matrices even with superlinear rigidity when $r=\Omega(n)$. In this paper, we construct an infinite family of complex matrices with the largest possible, i.e., $(n-r)^2$, rigidity. The entries of an $\nbyn$ matrix in this family are distinct primitive roots of unity of orders roughly \SL{$\exp(n^4 \log n)$}. To the best of our knowledge, this is the first family of concrete (but not entirely explicit) matrices having maximal rigidity and a succinct algebraic description. Our construction is based on elimination theory of polynomial ideals. In particular, we use results on the existence of polynomials in elimination ideals with effective degree upper bounds (effective Nullstellensatz). Using elementary algebraic geometry, we prove that the dimension of the affine variety of matrices of rigidity at most $k$ is exactly $n^2 - (n-r)^2 +k$. Finally, we use elimination theory to examine whether the rigidity function is semicontinuous.

In the multiparty communication game (CFL game) of Chandra, Furst, and Lipton [Proceedings of the 15th Annual ACM Symposium on Theory of Computing, Boston, MA, 1983, pp. 94--99] k players collaboratively evaluate a function f(x 0 , . . . , x k -1) in which player i knows all inputs except xi. The players have unlimited computational power. The objective is to minimize communication. In this paper, we study the SIMULTANEOUS MESSAGES (SM) model of multiparty communication complexity. The SM model is a restricted version of the CFL game in which the players are not allowed to communicate with each other. Instead, each of the k players simultaneously sends a message to a referee, who sees none of the inputs. The referee then announces the function value. We prove lower and upper bounds on the SM complexity of several classes of explicit functions. Our lower bounds extend to randomized SM complexity via an entropy argument. A lemma establishing a tradeoff between average Hamming distance and range size for transformations of the Boolean cube might be of independent interest. Our lower bounds on SM complexity imply an exponential gap between the SM model and the CFL model for up to $(\log n)^{1-\epsilon}$ players for any $\epsilon > 0$. This separation is obtained by comparing the respective complexities of the Generalized Addressing Function, GAF G,k , where G is a group of order n. We also combine our lower bounds on SM complexity with the ideas of Håstad and Goldmann [Comput. Complexity, 1 (1991), pp. 113--129] to derive superpolynomial lower bounds for certain depth-2 circuits computing a function related to the GAF function. We prove some counterintuitive upper bounds on SM complexity. We show that {\sf GAF}$_{\mathbb{Z}_2^t,3}$ has SM complexity $O(n^{0.92})$. When the number of players is at least $c\log n$, for some constant c > 0, our SM protocol for {\sf GAF}$_{\mathbb{Z}_2^t,k}$ has polylog(n) complexity. We also examine a class of functions defined by certain depth-2 circuits. This class includes the Generalized Inner Product function and Majority of Majorities. When the number of players is at least 2+log n, we obtain polylog(n) upper bounds for this class of functions.

An error-correcting code is said to be locally decodable if a randomized algorithm can recover any single bit of a message by reading only a small number of symbols of a possibly corrupted encoding of the message. Katz and Trevisan (2000) showed that any such code C: {0, 1} /spl rarr/ /spl Sigma//sup m/ with a decoding algorithm that makes at most q probes must satisfy m = /spl Omega/((n/log |/spl Sigma/|)/sup q/(q-1)/). They assumed that the decoding algorithm is non-adaptive, and left open the question of proving similar bounds for adaptive decoders. We improve the results of Katz and Trevisan (2000) in two ways. First, we give a more direct proof of their result. Second, and this is our main result, we prove that m = /spl Omega/((n/log|/spl Sigma/|)/sup q/(q-1)/) even if the decoding algorithm is adaptive. An important ingredient of our proof is a randomized method for smoothing an adaptive decoding algorithm. The main technical tool we employ is the Second Moment Method.

The rigidity of a matrix measures the number of entries that must be changed in order to reduce its rank below a certain value. The known lower bounds on the rigidity of explicit matrices are very weak. It is known that stronger lower bounds would have implications to complexity theory. We consider weaker forms of the rigidity problem over the complex numbers. Using spectral methods, we derive lower bounds on these variants. We then give two applications of such weaker forms. First, we show that our lower bound on a variant of rigidity implies lower bounds on size-depth tradeoffs for arithmetic circuits with bounded coefficients computing linear transformations. These bounds generalize a recent result of Nisan and Wigderson. The second application is conditional; we show that it would suffice to prove lower bounds on certain weaker forms of rigidity to conclude several separation results in communication complexity theory. Our results complement and strengthen a result of Razborov.