We study the problem of obtaining deterministic black-box polynomial identity testing algorithms (PIT) for algebraic branching programs (ABPs) that are read-once and oblivious. This class has an deterministic white-box polynomial identity testing algorithm (due to Raz and Shpilka), but prior to this work there was no known such black-box algorithm. The main result of this work gives the first quasi-polynomial sized hitting sets for size S circuits from this class, when the order of the variables is known. As our hitting set is of size exp(lg^2 S), this is analogous (in the terminology of boolean pseudorandomness) to a seed-length of lg^2 S, which is the seed length of the pseudorandom generators of Nisan and Impagliazzo-Nisan-Wigderson for read-once oblivious boolean branching programs. Our results are stronger for branching programs of bounded width, where we give a hitting set of size exp(lg^2 S lglg S), corresponding to a seed length of lg^2 S lglg S. This is in stark contrast to the known results for read-once oblivious boolean branching programs of bounded width, where no pseudorandom generator (or hitting set) with seed length o(lg^2 S) is known. In follow up work, we strengthened a result of Mulmuley, and showed that derandomizing a particular case of the Noether Normalization Lemma is reducible to black-box PIT of read-once oblivious ABPs. Using the results of the present work, this gives a derandomization of Noether Normalization in that case, which Mulmuley conjectured would difficult due to its relations to problems in algebraic geometry. We also show that several other circuit classes can be black-box reduced to read-once oblivious ABPs, including set-multilinear ABPs (a generalization of depth-3 set-multilinear formulas), non-commutative ABPs (generalizing non-commutative formulas), and (semi-)diagonal depth-4 circuits (as introduced by Saxena).
Similarly, the PIT problem has also been studied in the non-commutative model. While the work of Raz and Shpilka @cite_12 establishes a white-box PIT algorithm for non-commutative ABPs, the black-box PIT question for this model is more intricate since one cannot immediately apply the usual Schwartz-Zippel algorithm over non-commutative domains. However, Bogdanov and Wee @cite_13 showed how, leveraging the ideas in the Amitsur-Levitzki theorem @cite_18 , one can reduce non-commutative black-box PIT questions to commutative black-box PIT questions. By then appealing to Schwartz-Zippel, they give the first randomized algorithm for non-commutative PIT. They also discussed the possibility of derandomizing their result and raise a conjecture that if true would lead to a hitting set of size @math for non-commutative ABPs of size @math . Our Theorem gives a hitting set of size @math and does not require unproven assumptions.
@cite_18 @cite_13 @cite_12
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We study the problem of polynomial identity testing (PIT) for depth 2 arithmetic circuits over matrix algebra. We show that identity testing of depth 3 (SPS) arithmetic circuits over a field F is polynomial time equivalent to identity testing of depth 2 (PS) arithmetic circuits over U2(F), the algebra of upper-triangular 2 2 matrices with entries from F. Such a connection is a bit surprising since we also show that, as computational models, PS circuits over U2(F) are strictly ‘weaker’ than SPS circuits over F. The equivalence further implies that PIT of SPS circuits reduces to PIT of width-2 commutative Algebraic Branching Programs(ABP). Further, we give a deterministic polynomial time identity testing algorithm for a PS circuit of size s over commutative algebras of dimension O(log s log log s) over F. Over commutative algebras of dimension poly(s), we show that identity testing of PS circuits is at least as hard as that of SPS circuits over F. Mulmuley [Mul12a] recently gave an explicit version of Noether’s Normalization lemma for ring of invariants of matrices under simultaneous conjugation, under the conjecture that there are deterministic black-box algorithms for polynomial identity testing (PIT). He argued that this gives evidence that constructing such algorithms for PIT is beyond current techniques. In this work, we show this is not the case. That is, we improve Mulmuley’s reduction and correspondingly weaken the conjecture regarding PIT needed to give explicit Noether Normalization. We then observe that the weaker conjecture has recently been nearly settled by the authors ([FS12]), who gave quasipolynomial size hitting sets for the class of read-once oblivious algebraic branching programs (ROABPs). This gives the desired explicit Noether Normalization unconditionally, up to quasipolynomial factors. As a consequence of our proof we give a deterministic parallel polynomial-time algorithm for deciding if two matrix tuples have intersecting orbit closures, under simultaneous conjugation. We also study the strength of conjectures that Mulmuley requires to obtain similar results as ours. We prove that his conjectures are stronger, in the sense that the computational model he needs PIT algorithms for is equivalent to the well-known algebraic branching program (ABP) model, which is provably stronger than the ROABP model. Finally, we consider the depth-3 diagonal circuit model as defined by Saxena [Sax08], as PIT algorithms for this model also have implications in Mulmuley’s work. Previous work (such as [ASS12] and [FS12]) have given quasipolynomial size hitting sets for this model. In this work, we give a much simpler construction of such hitting sets, using techniques of Shpilka and Volkovich [SV09]. In this paper we present a deterministic polynomial time algorithm for testing if a symbolic matrix in non-commuting variables over @math is invertible or not. The analogous question for commuting variables is the celebrated polynomial identity testing (PIT) for symbolic determinants. In contrast to the commutative case, which has an efficient probabilistic algorithm, the best previous algorithm for the non-commutative setting required exponential time (whether or not randomization is allowed). The algorithm efficiently solves the "word problem" for the free skew field, and the identity testing problem for arithmetic formulae with division over non-commuting variables, two problems which had only exponential-time algorithms prior to this work. The main contribution of this paper is a complexity analysis of an existing algorithm due to Gurvits, who proved it was polynomial time for certain classes of inputs. We prove it always runs in polynomial time. The main component of our analysis is a simple (given the necessary known tools) lower bound on central notion of capacity of operators (introduced by Gurvits). We extend the algorithm to actually approximate capacity to any accuracy in polynomial time, and use this analysis to give quantitative bounds on the continuity of capacity (the latter is used in a subsequent paper on Brascamp-Lieb inequalities). Symbolic matrices in non-commuting variables, and the related structural and algorithmic questions, have a remarkable number of diverse origins and motivations. They arise independently in (commutative) invariant theory and representation theory, linear algebra, optimization, linear system theory, quantum information theory, approximation of the permanent and naturally in non-commutative algebra. We provide a detailed account of some of these sources and their interconnections. Using ideas from automata theory we design a new efficient (deterministic) identity test for the noncommutative polynomial identity testing problem (first introduced and studied in [RS05, BW05]). More precisely, given as input a noncommutative circuit C x1, ldrldrldr , xn computing a polynomial in F x1, ldrldrldr , xn of degree d with at most t monomials, where the variables xi are noncommuting, we give a deterministic polynomial identity test that checks if C equiv 0 and runs in time polynomial in d, n, |C|, and t. The same methods works in a black-box setting: given a noncommuting black-box polynomial f isin F x1, ldrldrldr , xn of degree d with t monomials we can, in fact, reconstruct the entire polynomial f in time polynomial in n, d and t. Indeed, we apply this idea to the reconstruction of black-box noncommuting algebraic branching programs (the ABPs considered by Nisan in [N91] and Raz-Shpilka in [RS05]). Assuming that the black-box model allows us to query the ABP for the output at any given gate then we can reconstruct an (equivalent) ABP in deterministic polynomial time. Finally, we turn to commutative identity testing and explore the complexity of the problem when the coefficients of the input polynomial come from an arbitrary finite commutative ring with unity whose elements are uniformly encoded as strings and the ring operations are given by an oracle. We show that several algorithmic results for polynomial identity testing over fields also hold when the coefficients come from such finite rings. We continue the study of noncommutative polynomial identity testing initiated by Raz and Shpilka and present efficient algorithms for the following problems in the noncommutative model: polynomial identity testing: The algorithm gets as an input an arithmetic circuit with the promise that the polynomial it computes has small degree (for instance, a circuit of logarithmic depth or an arithmetic formula) and determines whether or not the output of the circuit is identically zero (as a formal expression). Unlike the algorithm by Raz and Shpilka, our algorithm is black-box (but randomized with one-sided error) and evaluates the circuit over the ring of matrices. In addition, we present query complexity lower bounds for identity testing and explore the possibility of de-randomizing our algorithm. The analysis of our algorithm uses a noncommutative variant of the Schwartz-Zippel test. Minimizing algebraic branching programs: The algorithm gets as an input an algebraic branching program (ABP) and outputs a smallest equivalent ABP. The algorithm is based on Nisan's characterization of ABP complexity, and uses as a sub-routine an algorithm for computing linear dependencies amongst arithmetic formulas, a problem previously studied by the authors. In this paper we study polynomial identity testing of sums of @math read-once algebraic branching programs ( @math -RO-ABPs), generalizing the work in (Shpilka and Volkovich 2008,2009), who considered sums of @math read-once formulas ( @math -RO-formulas). We show that @math -RO-ABPs are strictly more powerful than @math -RO-formulas, for any @math , where @math is the number of variables. We obtain the following results: 1) Given free access to the RO-ABPs in the sum, we get a deterministic algorithm that runs in time @math , where @math bounds the size of any largest RO-ABP given on the input. This implies we have a deterministic polynomial time algorithm for testing whether the sum of a constant number of RO-ABPs computes the zero polynomial. 2) Given black-box access to the RO-ABPs computing the individual polynomials in the sum, we get a deterministic algorithm that runs in time @math . 3) Finally, given only black-box access to the polynomial computed by the sum of the @math RO-ABPs, we obtain an @math time deterministic algorithm. We study the problem of approximating the commuting-operator value of a two-player non-local game. It is well-known that it is @math -complete to decide whether the classical value of a non-local game is 1 or @math . Furthermore, as long as @math is small enough, this result does not depend on the gap @math . In contrast, a recent result of Fitzsimons, Ji, Vidick, and Yuen shows that the complexity of computing the quantum value grows without bound as the gap @math decreases. In this paper, we show that this also holds for the commuting-operator value of a game. Specifically, in the language of multi-prover interactive proofs, we show that the power of @math (proofs with two provers, one round, completeness probability @math , soundness probability @math , and commuting-operator strategies) can increase without bound as the gap @math gets arbitrarily small. Our results also extend naturally in two ways, to perfect zero-knowledge protocols, and to lower bounds on the complexity of computing the approximately-commuting value of a game. Thus we get lower bounds on the complexity class @math - @math of perfect zero-knowledge multi-prover proofs with approximately-commuting operator strategies, as the gap @math gets arbitrarily small. While we do not know any computable time upper bound on the class @math , a result of the first author and Vidick shows that for @math and @math , the class @math , with constant communication from the provers, is contained in @math . We give a lower bound of @math (ignoring constants inside the function) for this class, which is tight up to polynomial factors assuming the exponential time hypothesis. In this paper we consider particular equational formulae where equality = AC is the congruence induced by a set of associative-commutative axioms. The formulae we are interested in have the form t ? ACt1 ??t ? ACtn and are usually known as complement problems. To solve a complement problem is to find an instance of t which is not an instance of any ti modulo associativity-commutativity. We give a decision procedure based on tree automata which solves these formulae when all the ti1s are linear. We show that this solution also gives a decision of inductive reducibility modulo associativity and commutativity in the linear case and we give several extensions of this approach. Then, we define a new class of tree automata, called conditional tree automata which recognize sets of generalized terms, i.e terms written as multisets, and where the application of a rule depends on the satisfiability of a formula of Presburger's arithmetic. This class of tree automata allows one to solve non-linear complement problems when all occurrences of a non-linear variable occur under the same node (in flattened terms). This solution also provides a procedure to decide inductive reducibility modulo associativity-commutativity in the same case. We give a deterministic polynomial time algorithm for polynomial identity testing in the following two cases: 1. Non commutative arithmetic formulas: the algorithm gets as an input an arithmetic formula in the non-commuting variables x sub i ,...,x sub n and determines whether or not the output of the formula is identically 0 (as a formal expression). 2. Pure arithmetic circuits: the algorithm gets as an input a pure arithmetic circuit (as defined by N. Nisan and A. Wigderson (1996)) in the variables x sub i ,...,x sub n and determines whether or not the output of the circuit is identically 0 (as a formal expression). We also give a deterministic polynomial time identity testing algorithm for non commutative algebraic branching programs as defined by N. Nisan (1991). One application is a deterministic polynomial time identity testing for multilinear arithmetic circuits of depth 3. Finally, we observe an exponential lower bound for the size of pure arithmetic circuits for the permanent and for the determinant. (Only lower bounds for the depth of pure circuits were previously known by N. Nisan and A. Wigderson (1996). Tsirelson's problem asks whether the commuting operator model for two-party quantum correlations is equivalent to the tensor-product model. We give a negative answer to this question by showing that there are non-local games which have perfect commuting-operator strategies, but do not have perfect tensor-product strategies. The weak Tsirelson problem, which is known to be equivalent to Connes embedding problem, remains open. The examples we construct are instances of (binary) linear system games. For such games, previous results state that the existence of perfect strategies is controlled by the solution group of the linear system. Our main result is that every finitely-presented group embeds in some solution group. As an additional consequence, we show that the problem of determining whether a linear system game has a perfect commuting-operator strategy is undecidable. This paper develops techniques for establishing a lower bound on the number of arithmetic operations necessary for sets of simple expressions. The techniques are applied to matrix multiplication. A modification of Strassen's algorithm is developed for multiplying n × p matrices by p × q matrices. The techniques are used to prove that this algorithm minimizes the number of multiplications for a few special cases. In so doing we establish that matrix multiplication with elements from a commutative ring requires fewer multiplications than with elements from a non-commutative ring. We classify fully commutative elements in the affine Coxeter group of type @math . We give a normal form for such elements, then we propose an application of this normal form: we lift these fully commutative elements to the affine braid group of type @math and we get a form for "fully commutative braids". By introducing a quantitative degree of commutativity'' in terms of the angle between spin observables we present two tight quantitative trade-off relations in the case of two qubits. First, for entangled states, between the degree of commutativity of local observables and the maximal amount of violation of the Bell inequality: if both local angles increase from zero to @math (i.e., the degree of local commutativity decreases), the maximum violation of the Bell inequality increases. Secondly, a converse trade-off relation holds for separable states: if both local angles approach @math the maximal value obtainable for the correlations in the Bell inequality decreases and thus the non-violation increases. As expected, the extremes of these relations are found in the case of anticommuting local observables where, respectively, the bounds of @math and @math hold for the expectation value of the Bell operator. The trade-off relations show that noncommmutativity gives a more than classical result'' for entangled states, whereas a less than classical result'' is obtained for separable states. The experimental relevance of the trade-off relation for separable states is that it provides an experimental test for two qubit entanglement. Its advantages are twofold: in comparison to violations of Bell inequalities it is a stronger criterion and in comparison to entanglement witnesses it needs to make less strong assumptions about the observables implemented in the experiment. It was shown by Connes, Douglas, Schwarz [hep-th 9711162] that one can compactify M(atrix) theory on a non-commutative torus To. We prove that compactifications on Morita equivalent tori are in some sense physically equivalent. This statement can be considered as a generalization of non-classical SL(2,Z)N duality conjectured by Connes, Douglas and Schwarz for compactifications on two-dimensional non-commutative tori. This paper develops an algorithm to multiply a px2 matrix by a 2xn matrix in @math multiplications for matrix multiplication without commutativity. The algorithm minimizes the number of multiplications for matrix multiplication without commutativity for the special cases p=1 or 2, n=1,2, @math and p = 3, n = 3. It is shown that with commutativity fewer multiplications are required. We recently proposed a data structure, called associative-commutative discrimination nets, that supports efficient algorithms for (many-to-one) term matching in the presence of associative-commutative functions. In this paper we discuss the integration of such discrimination nets into an actual equational theorem prover and report on corresponding experiments. The general associative-commutative matching problem is known to be NP-complete, but can be solved in polynomial time if the given terms are linear, i.e., do not contain multiple occurrences of the same variable. We therefore have implemented a two-stage matching procedure. First we check whether a match exists for the linearized versions of the given terms (where different occurrences of the same variable are replaced by different new variables). If a match for the linearized terms does exist, we then determine whether there is also a match for the original, non-linear terms (i.e., whether the proposed substitutions for different occurrences of the same variable are consistent). Our experimental results indicate that this approach works very well in theorem proving, where most matching attempts actually fail and are filtered out during the first stage, so that the second, more expensive stage of the algorithm is only needed in comparatively few cases. Home-work commuting has always attracted significant research attention because of its impact on human mobility. One of the key assumptions in this domain of study is the universal uniformity of commute times. However, a true comparison of commute patterns has often been hindered by the intrinsic differences in data collection methods, which make observation from different countries potentially biased and unreliable. In the present work, we approach this problem through the use of mobile phone call detail records (CDRs), which offers a consistent method for investigating mobility patterns in wholly different parts of the world. We apply our analysis to a broad range of datasets, at both the country (Portugal, Ivory Coast, and Saudi Arabia), and city (Boston) scale. Additionally, we compare these results with those obtained from vehicle GPS traces in Milan. While different regions have some unique commute time characteristics, we show that the home-work time distributions and average values within a single region are indeed largely independent of commute distance or country (Portugal, Ivory Coast, and Boston)–despite substantial spatial and infrastructural differences. Furthermore, our comparative analysis demonstrates that such distance-independence holds true only if we consider multimodal commute behaviors–as consistent with previous studies. In car-only (Milan GPS traces) and car-heavy (Saudi Arabia) commute datasets, we see that commute time is indeed influenced by commute distance. Finally, we put forth a testable hypothesis and suggest ways for future work to make more accurate and generalizable statements about human commute behaviors. Depending on how much information an adversary can access to, adversarial attacks can be classified as white-box attack and black-box attack. In both cases, optimization-based attack algorithms can achieve relatively low distortions and high attack success rates. However, they usually suffer from poor time and query complexities, thereby limiting their practical usefulness. In this work, we focus on the problem of developing efficient and effective optimization-based adversarial attack algorithms. In particular, we propose a novel adversarial attack framework for both white-box and black-box settings based on the non-convex Frank-Wolfe algorithm. We show in theory that the proposed attack algorithms are efficient with an @math convergence rate. The empirical results of attacking Inception V3 model and ResNet V2 model on the ImageNet dataset also verify the efficiency and effectiveness of the proposed algorithms. More specific, our proposed algorithms attain the highest attack success rate in both white-box and black-box attacks among all baselines, and are more time and query efficient than the state-of-the-art. A novel class of derivative-free optimization algorithms is developed. The main idea is to utilize certain non-commutative maps in order to approximate the gradient of the objective function. Convergence properties of the novel algorithms are established and simulation examples are presented. Given an @math matrix @math , its Schatten- @math norm, @math , is defined as @math , where @math is the @math -th largest singular value of @math . These norms have been studied in functional analysis in the context of non-commutative @math -spaces, and recently in data stream and linear sketching models of computation. Basic questions on the relations between these norms, such as their embeddability, are still open. Specifically, given a set of matrices @math , suppose we want to construct a linear map @math such that @math for each @math , where @math and @math , and further, @math for a given approximation factor @math and real number @math . Then how large do @math and @math need to be as a function of @math ? We nearly resolve this question for every @math , for the case where @math can be expressed as @math , where @math and @math are arbitrary matrices that are allowed to depend on @math , that is, @math can be implemented by left and right matrix multiplication. Namely, for every @math , we provide nearly matching upper and lower bounds on the size of @math and @math as a function of @math . Importantly, our upper bounds are oblivious , meaning that @math and @math do not depend on the @math , while our lower bounds hold even if @math and @math depend on the @math . As an application of our upper bounds, we answer a recent open question of about space-approximation trade-offs for the Schatten @math -norm, showing in a data stream it is possible to estimate the Schatten- @math norm up to a factor of @math using @math space.