Testing Linear-Invariant Function Isomorphism

This paper addresses the state estimation problem for a class of two‐dimensional shift‐varying systems with time‐correlated multiplicative measurement noises and additive noises. The time‐correlated multiplicative noises can be described as a linear system model corrupted by white noise. The main objective of this paper is to propose a recursive iterative coupled new‐type estimators and design the estimated gain reasonably to guarantee the minimum error covariance at each step. First, two new‐type coupling estimators are designed by introducing a new term. The unbiasedness of the two estimators are guaranteed by resorting to the mathematical inductive approach. Subsequently, an effective estimation is proposed given initial conditions. Moreover, the boundedness performance is investigated of the error covariance, and an upper bound is given based on the spectral norm. Finally, two numerical example are presented as an example to show the effectiveness of the estimation algorithm.

In this paper we consider the semigroup generated by the self-maps on the open convex cone of positive definite matrices of translations, congruence transformations and matrix inversion that includes symplectic Hamiltonians and show that every member of the semigroup contracts any invariant metric distance inherited from a symmetric gauge function. This extends the results of Bougerol for the Riemannian metric and of Liverani–Wojtkowski for the Thompson part metric. A uniform upper bound of the Lipschitz contraction constant for a member of the semigroup is given in terms of the minimum eigenvalues of its determining matrices. We apply this result to a variety of nonlinear equations including Stein and Riccati equations for uniqueness and existence of positive definite solutions and find a new convergence analysis of iterative algorithms for the positive definite solution depending only on the least contraction coefficient for the invariant metric from the spectral norm.

In this paper, we provide a family of bounds for the rate at which the functions of many inputs can be computed in-network on general topologies. Going beyond simple symmetric functions where the output is invariant to the permutation of the operands, e.g., average, parity, we describe an algorithm that is analyzed to provide throughput bounds (both lower and upper) for the general functions. In particular, we analyze our algorithm when the function to be computed is given as a binary tree schema. Our lower bounds depend on schema parameters like the number of operands and graph parameters like the second largest eigenvalue of the transition matrix of simple random walk on network graph, the maximum and minimum degree of any node in the network. The lower bounding technique that we have used is based on the network flows and can capture general multi-commodity flow settings. Our proposed algorithm uses the well-known simple random walk on a network as its basic primitive for routing. We show that the lower bound obtained on the rate of computation is tight for the complete network topology, the hypercube and the star topology. We also present an upper bound on the expected latency of any data operand in terms of the height of schema, well-studied random walk parameter like the hitting time, and the relative distance from the critical data rate. For the computation time of symmetric functions on the random geometric graph under the gossip model, our approach achieves an order-optimal \(\widetilde{O}(n)\) time despite enforcing a binary tree schema for function computation. In general, Big-O notation represents an upper bound and \(\widetilde{O}\) hides \(\text {poly}\log n\) factors.

We consider dynamic evaluation of algebraic functions (matrix multiplication, determinant, convolution, Fourier transform, etc.) in the model of Reif and Tate; i.e., if f(x1, ..., xn) = (y1, ..., ym) is an algebraic problem, we consider serving on-line requests of the form change input xi to value v or what is the value of output yi?. We present techniques for showing lower bounds on the worst case time complexity per operation for such problems. The first gives lower bounds in a wide range of rather powerful models (for instance history dependent algebraic computation trees over any infinite subset of a field, the integer RAM, and the generalized real RAM model of Ben-Amram and Galil). Using this technique, we show optimal Ω(n) bounds for dynamic matrix-vector product, dynamic matrix multiplication and dynamic discriminant and an Ω(√n) lower bound for dynamic polynomial multiplication (convolution), providing a good match with Reif and Tate's O(√n log n) upper bound. We also show linear lower bounds for dynamic determinant, matrix adjoint and matrix inverse and an Ω(√n) lower bound for the elementary symmetric functions. The second technique is the communication complexity technique of Miltersen, Nisan, Safra, and Wigderson which we apply to the setting of dynamic algebraic problems, obtaining similar lower bounds in the word RAM model. The third technique gives lower bounds in the weaker straight line program model. Using this technique, we show an Ω((log n)2/log log n) lower bound for dynamic discrete Fourier transform. Technical ingredients of our techniques are the incompressibility technique of Ben-Amram and Galil and the lower bound for depth-two superconcentrators of Radhakrishnan and Ta-Shma. The incompressibility technique is extended to arithmetic computation in arbitrary fields. Due to the space constraints imposed by these proceedings, in this version of the paper we only present the third technique, proving the lower bound for dynamic discrete Fourier transform and refer to the full version of the paper which is currently available as a BRICS technical report, for the rest of the proofs.

This article initiates the study of testing properties of directed graphs. In particular, the article considers the most basic property of directed graphs--acyclicity. Because the choice of representation affects the choice of algorithm, the two main representations of graphs are studied. For the adjacency-matrix representation, most appropriate for dense graphs, a testing algorithm is developed that requires query and time complexity of O(1/e2), where e is a distance parameter independent of the size of the graph. The algorithm, which can probe the adjacency matrix of the graph, accepts every graph that is acyclic, and rejects, with probability at least 2/3, every graph whose adjacency matrix should be modified in at least e fraction of its entries so that it becomes acyclic. For the incidence list representation, most appropriate for sparse graphs, an Ω(|V|1/3) lower bound is proved on the number of queries and the time required for testing, where V is the set of vertices in the graph. Along with acyclicity, this article considers the property of strong connectivity. Contrasting upper and lower bounds are proved for the incidence list representation. In particular, if the testing algorithm can query on both incoming and outgoing edges at each vertex, then it is possible to test strong connectivity in O(1/e) time and query complexity. On the other hand, if the testing algorithm only has access to outgoing edges, then Ω(√N) queries are required to test for strong connectivity.

We show that if Arthur-Merlin protocols can be derandomized, then there is a Boolean function computable in deterministic exponential-time with access to an $\bm{\NP}$ oracle, that cannot be computed by Boolean circuits of {\em exponential} size. More formally, if $\bm{\prAM \subseteq \P^{\NP}}$ then there is a Boolean function in $\bm{\E^{\NP}}$ that requires circuits of size $\bm{2^{\Omega(n)}}$. $\bm{\prAM}$ is the class of promise problems that have Arthur-Merlin protocols, $\bm{\P^{\NP}}$ is the class of functions that can be computed in deterministic polynomial-time with an $\bm{\NP}$ oracle and $\bm{\E^{\NP}}$ is its exponential analogue. The lower bound in the conclusion of our theorem suffices to construct very strong pseudorandom generators. We also show that the same conclusion holds if the problem of approximate counting the number of accepting paths of a nondeterministic Turing machine up to multiplicative factors can be done in nondeterministic polynomial-time. In other words, showing nondeterministic fully polynomial-time approximation schemes for $\bm{\sharp \P}$-complete problems require proving exponential-size circuit lower bounds. A few works have already shown that if we can find efficient deterministic solutions to some specific tasks (or classes) that are known to be solvable efficiently by randomized algorithms (or proofs), then we obtain lower bounds against certain circuit models. These lower bounds were only with respect to polynomial-size circuits {\em even if full derandomization is assumed}. Thus they only implied fairly weak pseudorandom generators (if at all). A key ingredient in our proof is a connection between computational learning theory and exponential-size lower bounds. We show that the existence of deterministic learning algorithms with certain properties implies exponential-size lower bounds, where the complexity of the hard function is related to the complexity of the learning algorithm.

A comparison is made between two methods of parameter estimation for analysis of dynamic experiments in which the input function is noisy. Noise in the input function leads to uncertainties in the calculated model-predicted values, and therefore the covariance matrix of the residuals is a function of the model parameters. Statistical uncertainties in the model-predicted values significantly change the nature of the fitting process and the quality of the results. The initial method uses a weighted least-squares criterion where the weighting matrix is the inverse of the full covariance matrix of the residuals, incorporating both the noise in the output data and the noise in the input function. The methodology was applied to dynamic emission tomography studies of the heart, where the blood (input) and tissue (output) tracer concentrations at each time are derived from two regions of interest in the same tomographic section. The second method introduces additional parameters to describe the input function, and adds terms to the weighted sum of squares which comprise the criterion. Instead of only summing the weighted terms to account for differences between the model and the output function, there is a second set of terms to account for the differences between the model and the input function. The two methods have different theoretical bases and appear to optimize different criteria, but it is shown here that they are equivalent to one another. The criterion which they minimize is the same under certain matrix invertibility constraints, which must be satisfied to ensure the stability of either method.

We prove lower bounds on the randomized two-party communication complexity of functions that arise from read-once boolean formulae. A read-once boolean formula is a formula in propositional logic with the property that every variable appears exactly once. Such a formula can be represented by a tree, where the leaves correspond to variables, and the internal nodes are labeled by binary connectives. Under certain assumptions, this representation is unique. Thus, one can define the depth of a formula as the depth of the tree that represents it. The complexity of the evaluation of general read-once formulae, has attracted interest mainly in the decision tree model. In the communication complexity model, many interesting results deal with specific read-once formulae, such as DISJOINTNESS and TRIBES. In this paper we use information theory methods to prove lower bounds that hold for any read-once formula. Our lower bounds are of the form n(f)/c^{d(f)}, where n(f) is the number of variables and d(f) the depth of the formula, and they are optimal up to the constant c in the denominator.

The subcube partition model of computation is at least as powerful as decision trees but no separation between these models was known. We show that there exists a function whose deterministic subcube partition complexity is asymptotically smaller than its randomized decision tree complexity, resolving an open problem of Friedgut, Kahn, and Wigderson (2002). Our lower bound is based on the information-theoretic techniques first introduced to lower bound the randomized decision tree complexity of the recursive majority function. We also show that the public-coin partition bound, the best known lower bound method for randomized decision tree complexity subsuming other general techniques such as block sensitivity, approximate degree, randomized certificate complexity, and the classical adversary bound, also lower bounds randomized subcube partition complexity. This shows that all these lower bound techniques cannot prove optimal lower bounds for randomized decision tree complexity, which answers an open question of Jain and Klauck (2010) and Jain, Lee, and Vishnoi (2014).

The necessity of non-binary constraint satisfaction algorithms is increasing because many real problems are inherently nonbinary. Considering overconstrained problems (and Partial Forward Checking as the solving algorithm), we analyze several lower bounds proposed in the binary case, extending them for the non-binary case. We show that techniques initially developed in the context of reversible DAC can be applied in the general case, to deal with constraints of any arity. We discuss some of the issues raised for non-binary lower bounds, and we study their computational complexity. We provide experimental results of the use of the new lower bounds on overconstrained random problems, including constraints with different weights.KeywordsConstraint Satisfaction ProblemSoft ConstraintLimited VersionFuture VariableCurrent Partial AssignmentThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The primary problem in property testing is to decide whether a given function satisfies a certain property, or is far from any function satisfying it. This crucially requires a notion of distance between functions. The most prevalent notion is the Hamming distance over the {\em uniform} distribution on the domain. This restriction to uniformity is more a matter of convenience than of necessity, and it is important to investigate distances induced by more general distributions. In this paper, we make significant strides in this direction. We give simple and optimal testers for {\em bounded derivative properties} over {\em arbitrary product distributions}. Bounded derivative properties include fundamental properties such as monotonicity and Lipschitz continuity. Our results subsume almost all known results (upper and lower bounds) on monotonicity and Lipschitz testing. We prove an intimate connection between bounded derivative property testing and binary search trees (BSTs). We exhibit a tester whose query complexity is the sum of expected depths of optimal BSTs for each marginal. Furthermore, we show this sum-of-depths is also a lower bound. A fundamental technical contribution of this work is an {\em optimal dimension reduction theorem} for all bounded derivative properties, which relates the distance of a function from the property to the distance of restrictions of the function to random lines. Such a theorem has been elusive even for monotonicity for the past 15 years, and our theorem is an exponential improvement to the previous best known result.

We extend the line of research initiated by Fortnow and Klivans [6] that studies the relationship between efficient learning algorithms and circuit lower bounds. In [6], it was shown that if a Boolean circuit class \(\mathcal{C}\) has an efficient deterministic exact learning algorithm, (i.e. an algorithm that uses membership and equivalence queries) then \(\mathsf{EXP}^{\mathsf{NP}} \not \subseteq \mathsf{P/poly}[\mathcal{C}]\). Recently, in [14] EXP NP was replaced by DTIME(n ω(1)). Yet for the models of randomized exact learning or Valiant’s PAC learning, the best result so far is a lower bound against BPEXP (the exponential-time analogue of BPP). In this paper, we derive stronger lower bounds as well as some other consequences from randomized exact learning and PAC learning algorithms, answering an open question posed in [6] and [14]. In particular, we show that

This study explores the mathematical and computational characteristics of geometrically weighted circulant and symmetric geometric semicirculant matrices with the aim of identifying their potential as efficient structural tools in artificial intelligence (AI) architectures and data compressions. At the preliminary stage, a comprehensive mathematical framework was established, including the derivation of various matrix norms (such as spectral and Frobenius norms), determinants, and matrix inverses. The construction of these matrices is guided by Fibonacci numbers, whose intrinsic link to the golden ratio introduces a natural geometric decay pattern. This biologically inspired structure contributes to the balance, regularity, and interpretability of the resulting matrices, which are particularly well-suited for low-complexity modeling in AI systems. Subsequently, singular value decomposition (SVD) was employed to perform low-rank approximations, with a focus on evaluating information loss through Frobenius norm differences between original and reconstructed matrices. Techniques such as soft-thresholding and selective singular value removal were applied to assess data compression performance. Results demonstrated that symmetric geometric semicirculant matrices yielded smaller norm deviations, indicating superior data retention. Moreover, by tuning the geometric ratio parameter $ r $, further improvements in matrix compactness and fidelity were achieved, especially when reducing $ r $ to values like $ \frac{1}{4} $. These outcomes were visually confirmed through heatmap representations, highlighting the robustness and compression potential of the proposed matrices.

The departure from normality of a matrix is a real scalar that is impractical to compute if the matrix is large and its eigenvalues are unknown. A simple formula is presented for computing an upper bound for departure from normality in the Frobenius norm. This new upper bound is cheaper to compute than the one derived by Henrici [Numer. Math., 4 (1962), pp. 24–40]. Moreover, the new bound is sharp for Hermitian matrices, skew-Hermitian matrices and, in general, any matrix with eigenvalues that are horizontally or vertically aligned in the complex plane. In terms of applications, the new bound can be used in computing bounds for the spectral norm of matrix functions or bounds for the sensitivity of eigenvalues to matrix perturbations.

We consider the problem of testing graph cluster structure: given access to a graph G = (V, E), can we quickly determine whether the graph can be partitioned into a few clusters with good inner conductance, or is far from any such graph? This is a generalization of the well-studied problem of testing graph expansion, where one wants to distinguish between the graph having good expansion (i.e. being a good single cluster) and the graph having a sparse cut (i.e. being a union of at least two clusters). A recent work of Czumaj, Peng, and Sohler (STOC'15) gave an ingenious sublinear time algorithm for testing k-clusterability in time O(n^1/2 poly(k)). Their algorithm implicitly embeds a random sample of vertices of the graph into Euclidean space, and then clusters the samples based on estimates of Euclidean distances between the points. This yields a very efficient testing algorithm, but only works if the cluster structure is very strong: it is necessary to assume that the gap between conductances of accepted and rejected graphs is at least logarithmic in the size of the graph G. In this paper we show how one can leverage more refined geometric information, namely angles as opposed to distances, to obtain a sublinear time tester that works even when the gap is a sufficiently large constant. Our tester is based on the singular value decomposition of a natural matrix derived from random walk transition probabilities from a small sample of seed nodes. We complement our algorithm with a matching lower bound on the query complexity of testing clusterability. Our lower bound is based on a novel property testing problem, which we analyze using Fourier analytic tools. As a byproduct of our techniques, we also achieve new lower bounds for the problem of approximating MAX-CUT value in sublinear time.