The remote set problem on lattices

We initiate studying the Remote Set Problem (\({\mathsf{RSP}}\)) on lattices, which given a lattice asks to find a set of points containing a point which is far from the lattice. We show a polynomial-time deterministic algorithm that on rank n lattice \({\mathcal{L}}\) outputs a set of points, at least one of which is \({\sqrt{\log n / n} \cdot \rho(\mathcal{L})}\) -far from \({\mathcal{L}}\) , where \({\rho(\mathcal{L})}\) stands for the covering radius of \({\mathcal{L}}\) (i.e., the maximum possible distance of a point in space from \({\mathcal{L}}\)). As an application, we show that the covering radius problem with approximation factor \({\sqrt{n / \log n}}\) lies in the complexity class \({\mathsf{NP}}\) , improving a result of Guruswami et al. (Comput Complex 14(2): 90–121, 2005) by a factor of \({\sqrt{\log n}}\) .Our results apply to any \({\ell_p}\) norm for \({2 \leq p \leq \infty}\) with the same approximation factors (except a loss of \({\sqrt{\log \log n}}\) for \({p = \infty}\)). In addition, we show that the output of our algorithm for \({\mathsf{RSP}}\) contains a point whose \({\ell_2}\) distance from \({\mathcal{L}}\) is at least \({(\log n/n)^{1/p} \cdot \rho^{(p)}(\mathcal{L})}\) , where \({\rho^{(p)}(\mathcal{L})}\) is the covering radius of \({\mathcal{L}}\) measured with respect to the \({\ell_p}\) norm. The proof technique involves a theorem on balancing vectors due to Banaszczyk (Random Struct Algorithms 12(4):351–360, 1998) and the “six standard deviations” theorem of Spencer (Trans Am Math Soc 289(2):679–706, 1985).     

Incompressible Functions,Relative-Error Extractors,and the Power of Nondeterministic Reductions

A circuit C compresses a function \({f : \{0,1\}^n\rightarrow \{0,1\}^m}\) if given an input \({x\in \{0,1\}^n}\), the circuit C can shrink x to a shorter ?-bit string x′ such that later, a computationally unbounded solver D will be able to compute f(x) based on x′. In this paper we study the existence of functions which are incompressible by circuits of some fixed polynomial size \({s=n^c}\). Motivated by cryptographic applications, we focus on average-case \({(\ell,\epsilon)}\) incompressibility, which guarantees that on a random input \({x\in \{0,1\}^n}\), for every size s circuit \({C:\{0,1\}^n\rightarrow \{0,1\}^{\ell}}\) and any unbounded solver D, the success probability \({\Pr_x[D(C(x))=f(x)]}\) is upper-bounded by \({2^{-m}+\epsilon}\). While this notion of incompressibility appeared in several works (e.g., Dubrov and Ishai, STOC 06), so far no explicit constructions of efficiently computable incompressible functions were known. In this work, we present the following results:

1. (1)
   Assuming that E is hard for exponential size nondeterministic circuits, we construct a polynomial time computable boolean function \({f:\{0,1\}^n\rightarrow \{0,1\}}\) which is incompressible by size n ^c circuits with communication \({\ell=(1-o(1)) \cdot n}\) and error \({\epsilon=n^{-c}}\). Our technique generalizes to the case of PRGs against nonboolean circuits, improving and simplifying the previous construction of Shaltiel and Artemenko (STOC 14).
    
2. (2)
   We show that it is possible to achieve negligible error parameter \({\epsilon=n^{-\omega(1)}}\) for nonboolean functions. Specifically, assuming that E is hard for exponential size \({\Sigma_3}\)-circuits, we construct a nonboolean function \({f:\{0,1\}^n\rightarrow \{0,1\}^m}\) which is incompressible by size n ^c circuits with \({\ell=\Omega(n)}\) and extremely small \({\epsilon=n^{-c} \cdot 2^{-m}}\). Our construction combines the techniques of Trevisan and Vadhan (FOCS 00) with a new notion of relative error deterministic extractor which may be of independent interest.
    
3. (3)
   We show that the task of constructing an incompressible boolean function \({f:\{0,1\}^n\rightarrow \{0,1\}}\) with negligible error parameter \({\epsilon}\) cannot be achieved by “existing proof techniques”. Namely, nondeterministic reductions (or even \({\Sigma_i}\) reductions) cannot get \({\epsilon=n^{-\omega(1)}}\) for boolean incompressible functions. Our results also apply to constructions of standard Nisan-Wigderson type PRGs and (standard) boolean functions that are hard on average, explaining, in retrospect, the limitations of existing constructions. Our impossibility result builds on an approach of Shaltiel and Viola (STOC 08).
    

     

Automated competitive analysis of real-time scheduling with graph games

This paper is devoted to automatic competitive analysis of real-time scheduling algorithms for firm-deadline tasksets, where only completed tasks contribute some utility to the system. Given such a taskset \({\mathcal {T}}\), the competitive ratio of an on-line scheduling algorithm \({\mathcal {A}}\) for \({\mathcal {T}}\) is the worst-case utility ratio of \({\mathcal {A}}\) over the utility achieved by a clairvoyant algorithm. We leverage the theory of quantitative graph games to address the competitive analysis and competitive synthesis problems. For the competitive analysis case, given any taskset \({\mathcal {T}}\) and any finite-memory on-line scheduling algorithm \({\mathcal {A}}\), we show that the competitive ratio of \({\mathcal {A}}\) in \({\mathcal {T}}\) can be computed in polynomial time in the size of the state space of \({\mathcal {A}}\). Our approach is flexible as it also provides ways to model meaningful constraints on the released task sequences that determine the competitive ratio. We provide an experimental study of many well-known on-line scheduling algorithms, which demonstrates the feasibility of our competitive analysis approach that effectively replaces human ingenuity (required for finding worst-case scenarios) by computing power. For the competitive synthesis case, we are just given a taskset \({\mathcal {T}}\), and the goal is to automatically synthesize an optimal on-line scheduling algorithm \({\mathcal {A}}\), i.e., one that guarantees the largest competitive ratio possible for \({\mathcal {T}}\). We show how the competitive synthesis problem can be reduced to a two-player graph game with partial information, and establish that the computational complexity of solving this game is Np-complete. The competitive synthesis problem is hence in Np in the size of the state space of the non-deterministic labeled transition system encoding the taskset. Overall, the proposed framework assists in the selection of suitable scheduling algorithms for a given taskset, which is in fact the most common situation in real-time systems design.     

Temperley–Lieb algebra,Yang-Baxterization and universal gate

A method of constructing n ² × n ² matrix realization of Temperley–Lieb algebras is presented. The single loop of these realizations are \({d=\sqrt{n}}\). In particular, a 9 × 9-matrix realization with single loop \({d=\sqrt{3}}\) is discussed. A unitary Yang–Baxter \({\breve{R}\theta,q_{1},q_{2})}\) matrix is obtained via the Yang-Baxterization process. The entanglement properties and geometric properties (i.e., Berry Phase) of this Yang–Baxter system are explored.     

Mutually unbiased maximally entangled bases in $$\mathbb {C}^d\otimes \mathbb {C}^d$$

We study mutually unbiased maximally entangled bases (MUMEB’s) in bipartite system \(\mathbb {C}^d\otimes \mathbb {C}^d (d \ge 3)\). We generalize the method to construct MUMEB’s given in Tao et al. (Quantum Inf Process 14:2291–2300, 2015), by using any commutative ring R with d elements and generic character of \((R,+)\) instead of \(\mathbb {Z}_d=\mathbb {Z}/d\mathbb {Z}\). Particularly, if \(d=p_1^{a_1}p_2^{a_2}\ldots p_s^{a_s}\) where \(p_1, \ldots , p_s\) are distinct primes and \(3\le p_1^{a_1}\le \cdots \le p_s^{a_s}\), we present \(p_1^{a_1}-1\) MUMEB’s in \(\mathbb {C}^d\otimes \mathbb {C}^d\) by taking \(R=\mathbb {F}_{p_1^{a_1}}\oplus \cdots \oplus \mathbb {F}_{p_s^{a_s}}\), direct sum of finite fields (Theorem 3.3).     

Quantum Query Complexity of Almost All Functions with Fixed On-set Size

This paper considers the quantum query complexity of almost all functions in the set \({\mathcal{F}}_{N,M}\) of \({N}\)-variable Boolean functions with on-set size \({M (1\le M \le 2^{N}/2)}\), where the on-set size is the number of inputs on which the function is true. The main result is that, for all functions in \({\mathcal{F}}_{N,M}\) except its polynomially small fraction, the quantum query complexity is \({ \Theta\left(\frac{\log{M}}{c + \log{N} - \log\log{M}} + \sqrt{N}\right)}\) for a constant \({c > 0}\). This is quite different from the quantum query complexity of the hardest function in \({\mathcal{F}}_{N,M}\): \({\Theta\left(\sqrt{N\frac{\log{M}}{c + \log{N} - \log\log{M}}} + \sqrt{N}\right)}\). In contrast, almost all functions in \({\mathcal{F}}_{N,M}\) have the same randomized query complexity \({\Theta(N)}\) as the hardest one, up to a constant factor.     

Collapse of the Hierarchy of Constant-Depth Exact Quantum Circuits

We study the quantum complexity class \({\mathsf{QNC}^\mathsf{0}_\mathsf{f}}\) of quantum operations implementable exactly by constant-depth polynomial-size quantum circuits with unbounded fan-out gates. Our main result is that the quantum OR operation is in \({\mathsf{QNC}^\mathsf{0}_\mathsf{f}}\), which is an affirmative answer to the question posed by Høyer and ?palek. In sharp contrast to the strict hierarchy of the classical complexity classes: \({\mathsf{NC}^{0} \subsetneq \mathsf{AC}^{0} \subsetneq \mathsf{TC}^{0}}\), our result with Høyer and ?palek’s one implies the collapse of the hierarchy of the corresponding quantum ones: \({\mathsf{QNC}^\mathsf{0}_\mathsf{f}=\mathsf{QAC}^\mathsf{0}_\mathsf{f}=\mathsf{QTC}^\mathsf{0}_\mathsf{f}}\). Then, we show that there exists a constant-depth subquadratic-size quantum circuit for the quantum threshold operation. This allows us to obtain a better bound on the size difference between the \({\mathsf{QNC}^\mathsf{0}_\mathsf{f}}\) and \({\mathsf{QTC}^\mathsf{0}_\mathsf{f}}\) circuits for implementing the same operation. Lastly, we show that, if the quantum Fourier transform modulo a prime is in \({\mathsf{QNC}^\mathsf{0}_\mathsf{f}}\), there exists a polynomial-time exact classical algorithm for a discrete logarithm problem using a \({\mathsf{QNC}^\mathsf{0}_\mathsf{f}}\) oracle. This implies that, under a plausible assumption, there exists a classically hard problem that is solvable exactly by a \({\mathsf{QNC}^\mathsf{0}_\mathsf{f}}\) circuit with gates for the quantum Fourier transform.     

A Framework for Certified Boolean Branch-and-Bound Optimization

We consider optimization problems of the form (S, cost), where S is a clause set over Boolean variables x ₁?...?x _n , with an arbitrary cost function \(\mathit{cost}\colon \mathbb{B}^n \rightarrow \mathbb{R}\), and the aim is to find a model A of S such that cost(A) is minimized. Here we study the generation of proofs of optimality in the context of branch-and-bound procedures for such problems. For this purpose we introduce \(\mathtt{DPLL_{BB}}\), an abstract DPLL-based branch-and-bound algorithm that can model optimization concepts such as cost-based propagation and cost-based backjumping. Most, if not all, SAT-related optimization problems are in the scope of \(\mathtt{DPLL_{BB}}\). Since many of the existing approaches for solving these problems can be seen as instances, \(\mathtt{DPLL_{BB}}\) allows one to formally reason about them in a simple way and exploit the enhancements of \(\mathtt{DPLL_{BB}}\) given here, in particular its uniform method for generating independently verifiable optimality proofs.     

Cubature formulae for nearly singular and highly oscillating integrals

The paper deals with the approximation of integrals of the type

$$\begin{aligned} I(f;{\mathbf {t}})=\int _{{\mathrm {D}}} f({\mathbf {x}}) {\mathbf {K}}({\mathbf {x}},{\mathbf {t}}) {\mathbf {w}}({\mathbf {x}}) d{\mathbf {x}},\quad \quad {\mathbf {x}}=(x_1,x_2),\quad {\mathbf {t}}\in \mathrm {T}\subseteq \mathbb {R}^p, \ p\in \{1,2\} \end{aligned}$$

where \({\mathrm {D}}=[-\,1,1]^2\), f is a function defined on \({\mathrm {D}}\) with possible algebraic singularities on \(\partial {\mathrm {D}}\), \({\mathbf {w}}\) is the product of two Jacobi weight functions, and the kernel \({\mathbf {K}}\) can be of different kinds. We propose two cubature rules determining conditions under which the rules are stable and convergent. Along the paper we diffusely treat the numerical approximation for kernels which can be nearly singular and/or highly oscillating, by using a bivariate dilation technique. Some numerical examples which confirm the theoretical estimates are also proposed.

     

Lower Bounds for Depth-Three Arithmetic Circuits with small bottom fanin

Shpilka & Wigderson (IEEE conference on computational complexity, vol 87, 1999) had posed the problem of proving exponential lower bounds for (nonhomogeneous) depth-three arithmetic circuits with bounded bottom fanin over a field \({{\mathbb{F}}}\) of characteristic zero. We resolve this problem by proving a \({N^{\Omega(\frac{d}{\tau})}}\) lower bound for (nonhomogeneous) depth-three arithmetic circuits with bottom fanin at most \({\tau}\) computing an explicit \({N}\)-variate polynomial of degree \({d}\) over \({{\mathbb{F}}}\). Meanwhile, Nisan & Wigderson (Comp Complex 6(3):217–234, 1997) had posed the problem of proving super-polynomial lower bounds for homogeneous depth-five arithmetic circuits. Over fields of characteristic zero, we show a lower bound of \({N^{\Omega(\sqrt{d})}}\) for homogeneous depth-five circuits (resp. also for depth-three circuits) with bottom fanin at most \({N^{\mu}}\), for any fixed \({\mu < 1}\). This resolves the problem posed by Nisan and Wigderson only partially because of the added restriction on the bottom fanin (a general homogeneous depth-five circuit has bottom fanin at most \({N}\)).     

Entanglement witness criteria of strong-<Emphasis Type="Italic">k</Emphasis>-separability for multipartite quantum states

Let \(H_{1}, H_{2},\ldots ,H_{n}\) be separable complex Hilbert spaces with \(\dim H_{i}\ge 2\) and \(n\ge 2\). Assume that \(\rho \) is a state in \(H=H_1\otimes H_2\otimes \cdots \otimes H_n\). \(\rho \) is called strong-k-separable \((2\le k\le n)\) if \(\rho \) is separable for any k-partite division of H. In this paper, an entanglement witnesses criterion of strong-k-separability is obtained, which says that \(\rho \) is not strong-k-separable if and only if there exist a k-division space \(H_{m_{1}}\otimes \cdots \otimes H_{m_{k}}\) of H, a finite-rank linear elementary operator positive on product states \(\Lambda :\mathcal {B}(H_{m_{2}}\otimes \cdots \otimes H_{m_{k}})\rightarrow \mathcal {B}(H_{m_{1}})\) and a state \(\rho _{0}\in \mathcal {S}(H_{m_{1}}\otimes H_{m_{1}})\), such that \(\mathrm {Tr}(W\rho )<0\), where \(W=(\mathrm{Id}\otimes \Lambda ^{\dagger })\rho _{0}\) is an entanglement witness. In addition, several different methods of constructing entanglement witnesses for multipartite states are also given.     

Partially Ordered Connectives and Monadic Monotone Strict NP

Motivated by constraint satisfaction problems, Feder and Vardi (SIAM Journal of Computing, 28, 57–104, 1998) set out to search for fragments \({\mathcal{L}}\) of \(\Sigma_1^1\) satisfying the dichotomy property: every problem definable in \({\mathcal{L}}\) is either in P or else NP-complete. Feder and Vardi considered in this connection two logics, strict NP (or SNP) and monadic, monotone, strict NP without inequalities (or MMSNP). The former consists of formulas of the form \(\exists \vec{X}\forall \vec{x} \phi\), where \(\phi\) is a quantifier-free formula in a relational vocabulary; and the latter is the fragment of SNP whose formulas involve only negative occurrences of relation symbols, only monadic second-order quantifiers, and no occurrences of the equality symbol. It remains an open problem whether MMSNP enjoys the dichotomy property. In the present paper, SNP and MMSNP are characterized in terms of partially ordered connectives. More specifically, SNP is characterized using the logic D of partially ordered connectives introduced in Blass and Gurevich (Annals of Pure and Applied Logic, 32, 1–16, 1986), Sandu and Väänänen (Zeitschrift für Mathematische Logik und Grundlagen der Mathematik, 38, 361–372 1992), and MMSNP employing a generalization C of D introduced in the present paper.     

The identity problem of finitely generated bi-ideals

Finitely generated bi-ideals with letters from a selected alphabet A are considered. We solve the equivalence problem for generating systems of bi-ideals, i.e., look for an effective procedure which provides the means of determining if two generating systems \({\langle u_0, . . . , u_{m-1} \rangle}\) and \({\langle v_0, . . . , v_{n-1} \rangle}\) represent equal or different bi-ideals. We offer a method of constructing, for every generating system \({\langle u_0, . . . , u_{m-1} \rangle}\) , an equivalent generating system \({\langle u^{\prime}_{0}, . . . , u^{\prime}_{m-1} \rangle}\) with differing members. We also describe an algorithm for deciding if two generating systems \({\langle u_0, u_1 \rangle}\) and \({\langle v_0, v_1 \rangle}\) are equivalent or not. For a general case, the problem of existence of such an algorithm remains open.     

Outsourced pattern matching

In secure delegatable computation, computationally weak devices (or clients) wish to outsource their computation and data to an untrusted server in the cloud. While most earlier work considers the general question of how to securely outsource any computation to the cloud server, we focus on concrete and important functionalities and give the first protocol for the pattern matching problem in the cloud. Loosely speaking, this problem considers a text T that is outsourced to the cloud \({\textsc {S}}\) by a sender \({\textsc {SEN}}\). In a query phase, receivers \({\textsc {REC}}_1, \ldots , {\textsc {REC}}_l\) run an efficient protocol with the server \({\textsc {S}}\) and the sender \({\textsc {SEN}}\) in order to learn the positions at which a pattern of length m matches the text (and nothing beyond that). This is called the outsourced pattern matching problem which is highly motivated in the context of delegatable computing since it offers storage alternatives for massive databases that contain confidential data (e.g., health-related data about patient history). Our constructions are simulation-based secure in the presence of semi-honest and malicious adversaries (in the random oracle model) and limit the communication in the query phase to O(m) bits plus the number of occurrences—which is optimal. In contrast to generic solutions for delegatable computation, our schemes do not rely on fully homomorphic encryption but instead use novel ideas for solving pattern matching, based on a reduction to the subset sum problem. Interestingly, we do not rely on the hardness of the problem, but rather we exploit instances that are solvable in polynomial time. A follow-up result demonstrates that the random oracle is essential in order to meet our communication bound.     

Least common container of tree pattern queries and its applications

Tree patterns represent important fragments of XPath. In this paper, we show that some classes \({\mathcal{C}}\) of tree patterns exhibit such a property that, given a finite number of compatible tree patterns \({P_1, \ldots, P_n\in \mathcal{C}}\), there exists another pattern P such that P ₁, . . . , P _n are all contained in P, and for any tree pattern \({Q\in \mathcal{C}}\), P ₁, . . . , P _n are all contained in Q if and only if P is contained in Q. We experimentally demonstrate that the pattern P is usually much smaller than P ₁, . . . , P _n combined together. Using the existence of P above, we show that testing whether a tree pattern, P, is contained in another, \({Q\in \mathcal{C}}\), under an acyclic schema graph G, can be reduced to testing whether P _G , a transformed version of P, is contained in Q without any schema graph, provided that the distinguished node of P is not labeled *. We then show that, under G, the maximal contained rewriting (MCR) of a tree pattern Q using a view V can be found by finding the MCR of Q using V _G without G, when there are no *-nodes on the distinguished path of V and no *-nodes in Q.     

Factors of low individual degree polynomials

Kaltofen (Randomness in computation, vol 5, pp 375–412, 1989) proved the remarkable fact that multivariate polynomial factorization can be done efficiently, in randomized polynomial time. Still, more than twenty years after Kaltofen’s work, many questions remain unanswered regarding the complexity aspects of polynomial factorization, such as the question of whether factors of polynomials efficiently computed by arithmetic formulas also have small arithmetic formulas, asked in Kopparty et al. (2014), and the question of bounding the depth of the circuits computing the factors of a polynomial. We are able to answer these questions in the affirmative for the interesting class of polynomials of bounded individual degrees, which contains polynomials such as the determinant and the permanent. We show that if \({P(x_{1},\ldots,x_{n})}\) is a polynomial with individual degrees bounded by r that can be computed by a formula of size s and depth d, then any factor \({f(x_{1},\ldots, x_{n})}\) of \({P(x_{1},\ldots,x_{n})}\) can be computed by a formula of size \({\textsf{poly}((rn)^{r},s)}\) and depth d + 5. This partially answers the question above posed in Kopparty et al. (2014), who asked if this result holds without the dependence on r. Our work generalizes the main factorization theorem from Dvir et al. (SIAM J Comput 39(4):1279–1293, 2009), who proved it for the special case when the factors are of the form \({f(x_{1}, \ldots, x_{n}) \equiv x_{n} - g(x_{1}, \ldots, x_{n-1})}\). Along the way, we introduce several new technical ideas that could be of independent interest when studying arithmetic circuits (or formulas).     

In All But Finitely Many Possible Worlds: Model-Theoretic Investigations on ‘<Emphasis Type="Italic">Overwhelming Majority</Emphasis>’ Default Conditionals

Defeasible conditionals are statements of the form ‘if A then normally B’. One plausible interpretation introduced in nonmonotonic reasoning dictates that (\(A\Rightarrow B\)) is true iff B is true in ‘most’ A-worlds. In this paper, we investigate defeasible conditionals constructed upon a notion of ‘overwhelming majority’, defined as ‘truth in a cofinite subset of \(\omega \)’, the first infinite ordinal. One approach employs the modal logic of the frame \((\omega , <)\), used in the temporal logic of discrete linear time. We introduce and investigate conditionals, defined modally over \((\omega , <)\); several modal definitions of the conditional connective are examined, with an emphasis on the nonmonotonic ones. An alternative interpretation of ‘majority’ as sets cofinal (in \(\omega \)) rather than cofinite (subsets of \(\omega \)) is examined. For these modal approaches over \((\omega , <)\), a decision procedure readily emerges, as the modal logic \({\mathbf {K4DLZ}}\) of this frame is well-known and a translation of the conditional sentences can be mechanically checked for validity; this allows also for a quick proof of \(\mathsf {NP}\)-completeness of the satisfiability problem for these logics. A second approach employs the conditional version of Scott-Montague semantics, in the form of \(\omega \)-many possible worlds, endowed with neighborhoods populated by collections of cofinite subsets of \(\omega \). This approach gives rise to weak conditional logics, as expected. The relative strength of the conditionals introduced is compared to (the conditional logic ‘equivalent’ of) KLM logics and other conditional logics in the literature.     

Kolmogorov Width of Discrete Linear Spaces: an Approach to Matrix Rigidity

A square matrix V is called rigid if every matrix \({V^\prime}\) obtained by altering a small number of entries of V has sufficiently high rank. While random matrices are rigid with high probability, no explicit constructions of rigid matrices are known to date. Obtaining such explicit matrices would have major implications in computational complexity theory. One approach to establishing rigidity of a matrix V is to come up with a property that is satisfied by any collection of vectors arising from a low-dimensional space, but is not satisfied by the rows of V even after alterations. In this paper, we propose such a candidate property that has the potential of establishing rigidity of combinatorial design matrices over the field \({\mathbb{F}_2.}\) Stated informally, we conjecture that under a suitable embedding of \({\mathbb{F}_2^n}\) into \({\mathbb{R}^n,}\) vectors arising from a low-dimensional \({\mathbb{F}_2}\)-linear space always have somewhat small Kolmogorov width, i.e., admit a non-trivial simultaneous approximation by a low-dimensional Euclidean space. This implies rigidity of combinatorial designs, as their rows do not admit such an approximation even after alterations. Our main technical contribution is a collection of results establishing weaker forms and special cases of the conjecture above.     

Parallel Multi-Block ADMM with <Emphasis Type="Italic">o</Emphasis>(1 / <Emphasis Type="Italic">k</Emphasis>) Convergence

This paper introduces a parallel and distributed algorithm for solving the following minimization problem with linear constraints:

$$\begin{aligned} \text {minimize} ~~&f_1(\mathbf{x}_1) + \cdots + f_N(\mathbf{x}_N)\\ \text {subject to}~~&A_1 \mathbf{x}_1 ~+ \cdots + A_N\mathbf{x}_N =c,\\&\mathbf{x}_1\in {\mathcal {X}}_1,~\ldots , ~\mathbf{x}_N\in {\mathcal {X}}_N, \end{aligned}$$

where \(N \ge 2\), \(f_i\) are convex functions, \(A_i\) are matrices, and \({\mathcal {X}}_i\) are feasible sets for variable \(\mathbf{x}_i\). Our algorithm extends the alternating direction method of multipliers (ADMM) and decomposes the original problem into N smaller subproblems and solves them in parallel at each iteration. This paper shows that the classic ADMM can be extended to the N-block Jacobi fashion and preserve convergence in the following two cases: (i) matrices \(A_i\) are mutually near-orthogonal and have full column-rank, or (ii) proximal terms are added to the N subproblems (but without any assumption on matrices \(A_i\)). In the latter case, certain proximal terms can let the subproblem be solved in more flexible and efficient ways. We show that \(\Vert {\mathbf {x}}^{k+1} - {\mathbf {x}}^k\Vert _M^2\) converges at a rate of o(1 / k) where M is a symmetric positive semi-definte matrix. Since the parameters used in the convergence analysis are conservative, we introduce a strategy for automatically tuning the parameters to substantially accelerate our algorithm in practice. We implemented our algorithm (for the case ii above) on Amazon EC2 and tested it on basis pursuit problems with >300 GB of distributed data. This is the first time that successfully solving a compressive sensing problem of such a large scale is reported.