Tractable Class Research Articles

A Long-Lived Halogen-Bonding Anion Triple Helicate Accommodates Rapid Guest Exchange.

Anion-templated helical structures are emerging as a dynamic and tractable class of supramolecules that exhibit anion-switchable self-assembly. We present the first kinetic studies of an anion helicate by utilizing halogen-bonding m-arylene-ethynylene oligomers. These ligands formed high-fidelity triple helicates in solution with surprisingly long lifetimes on the order of seconds even at elevated temperatures. We propose an associative ligand-exchange mechanism that proceeded slowly on the same timescale. In contrast, intrachannel anion exchange occurred rapidly within milliseconds or faster as determined by stopped-flow visible spectroscopy. Additionally, the helicate accommodated bromide in solution and the solid state, while the thermodynamic stability of the triplex favored larger halide ions (bromide≈iodide≫chloride). Taken together, we elucidate a new class of kinetically stable helicates. These anion-switchable triplexes maintain their architectures while accommodating fast intrachannel guest exchange.

A Long‐Lived Halogen‐Bonding Anion Triple Helicate Accommodates Rapid Guest Exchange

Anion-templated helical structures are emerging as a dynamic and tractable class of supramolecules that exhibit anion-switchable self-assembly. We present the first kinetic studies of an anion helicate by utilizing halogen-bonding m-arylene-ethynylene oligomers. These ligands formed high-fidelity triple helicates in solution with surprisingly long lifetimes on the order of seconds even at elevated temperatures. We propose an associative ligand-exchange mechanism that proceeded slowly on the same timescale. In contrast, intrachannel anion exchange occurred rapidly within milliseconds or faster as determined by stopped-flow visible spectroscopy. Additionally, the helicate accommodated bromide in solution and the solid state, while the thermodynamic stability of the triplex favored larger halide ions (bromide≈iodide≫chloride). Taken together, we elucidate a new class of kinetically stable helicates. These anion-switchable triplexes maintain their architectures while accommodating fast intrachannel guest exchange.

Binary constraint satisfaction problems defined by excluded topological minors

The binary Constraint Satisfaction Problem (CSP) is to decide whether there exists an assignment to a set of variables which satisfies specified constraints between pairs of variables. A binary CSP instance can be presented as a labelled graph encoding both the forms of the constraints and where they are imposed. We consider subproblems defined by restricting the allowed form of this graph. One type of restriction is to forbid certain specified substructures (patterns). This captures some tractable classes of the CSP, but does not capture classes defined by language restrictions, or the well-known structural property of acyclicity.We extend the notion of pattern and introduce the notion of a topological minor of a binary CSP instance. By forbidding a finite set of patterns from occurring as topological minors we obtain a compact mechanism for expressing novel tractable subproblems of the CSP, including new generalisations of the class of acyclic instances.

Efficient Evaluation and Static Analysis for Well-Designed Pattern Trees with Projection

Conjunctive queries (CQs) fail to provide an answer when the pattern described by the query does not exactly match the data. CQs might thus be too restrictive as a querying mechanism when data is semistructured or incomplete. The semantic web therefore provides a formalism—known as (projected) well-designed pattern trees (pWDPTs)—that tackles this problem: pWDPTs allow us to formulate queries that match parts of the query over the data if available, but do not ignore answers of the remaining query otherwise. Here we abstract away the specifics of semantic web applications and study pWDPTs over arbitrary relational schemas. Since the language of pWDPTs subsumes CQs, their evaluation problem is intractable. We identify structural properties of pWDPTs that lead to (fixed-parameter) tractability of various variants of the evaluation problem. We also show that checking if a pWDPT is equivalent to one in our tractable class is in 2EXPTIME. As a corollary, we obtain fixed-parameter tractability of evaluation for pWDPTs with such good behavior. Our techniques also allow us to develop a theory of approximations for pWDPTs.

On a new extension of BTP for binary CSPs

The study of broken-triangles is becoming increasingly ambitious, by both solving constraint satisfaction problems (CSPs) in polynomial time and reducing search space size through either value merging or variable elimination. Considerable progress has been made in extending this important concept, such as dual broken-triangle and weakly broken-triangle, in order to maximize the number of captured tractable CSP instances and/or the number of merged values. Specifically, m-wBTP allows us to merge more values than BTP. DBTP, ∀∃-BTP, k-BTP, WBTP and m-wBTP permit us to capture more tractable instances than BTP. However, except BTP, none of these extensions allows variable elimination while preserving satisfiability. Moreover, k-BTP and m-wBTP define bigger tractable classes around BTP but both of them generally need a high level of consistency. Here, we introduce a new weaker form of BTP, called m-fBTP for flexible broken-triangle property, which will represent a compromise between most of these previous tractable properties based on BTP. m-fBTP allows us on the one hand to eliminate more variables than BTP while preserving satisfiability and on the other to define a new bigger tractable class for which arc consistency is a decision procedure. Likewise, m-fBTP permits to merge more values than BTP but fewer than m-wBTP. The binary CSP instances satisfying m-fBTP are solved by algorithms of the state-of-the-art like MAC and RFL in polynomial time. An open question is whether it is possible to compute, in polynomial time, the existence of some variable ordering for which a given instance satisfies m-fBTP.

Bayesian nonparametric inference beyond the Gibbs‐type framework

AbstractThe definition and investigation of general classes of nonparametric priors has recently been an active research line in Bayesian statistics. Among the various proposals, the Gibbs‐type family, which includes the Dirichlet process as a special case, stands out as the most tractable class of nonparametric priors for exchangeable sequences of observations. This is the consequence of a key simplifying assumption on the learning mechanism, which, however, has justification except that of ensuring mathematical tractability. In this paper, we remove such an assumption and investigate a general class of random probability measures going beyond the Gibbs‐type framework. More specifically, we present a nonparametric hierarchical structure based on transformations of completely random measures, which extends the popular hierarchical Dirichlet process. This class of priors preserves a good degree of tractability, given that we are able to determine the fundamental quantities for Bayesian inference. In particular, we derive the induced partition structure and the prediction rules and characterize the posterior distribution. These theoretical results are also crucial to devise both a marginal and a conditional algorithm for posterior inference. An illustration concerning prediction in genomic sequencing is also provided.

Continuous tenor extension of affine LIBOR models with multiple curves and applications to XVA

We consider the class of affine LIBOR models with multiple curves, which is an analytically tractable class of discrete tenor models that easily accommodates positive or negative interest rates and positive spreads. By introducing an interpolating function, we extend the affine LIBOR models to a continuous tenor and derive expressions for the instantaneous forward rate and the short rate. We show that the continuous tenor model is arbitrage-free, that the analytical tractability is retained under the spot martingale measure, and that under mild conditions an interpolating function can be found such that the extended model fits any initial forward curve. This allows us to compute value adjustments (i.e. XVAs) consistently, by solving the corresponding ‘pre-default’ BSDE. As an application, we compute the price and value adjustments for a basis swap, and study the model risk associated to different interpolating functions.

SOS-Convex Semialgebraic Programs and its Applications to Robust Optimization: A Tractable Class of Nonsmooth Convex Optimization

In this paper, we introduce a new class of nonsmooth convex functions called SOS-convex semialgebraic functions extending the recently proposed notion of SOS-convex polynomials. This class of nonsmooth convex functions covers many common nonsmooth functions arising in the applications such as the Euclidean norm, the maximum eigenvalue function and the least squares functions with $\ell_1$-regularization or elastic net regularization used in statistics and compressed sensing. We show that, under commonly used strict feasibility conditions, the optimal value and an optimal solution of SOS-convex semi-algebraic programs can be found by solving a single semi-definite programming problem (SDP). We achieve the results by using tools from semi-algebraic geometry, convex-concave minimax theorem and a recently established Jensen inequality type result for SOS-convex polynomials. As an application, we outline how the derived results can be applied to show that robust SOS-convex optimization problems under restricted spectrahedron data uncertainty enjoy exact SDP relaxations. This extends the existing exact SDP relaxation result for restricted ellipsoidal data uncertainty and answers the open questions left in [Optimization Letters 9, 1-18(2015)] on how to recover a robust solution from the semi-definite programming relaxation in this broader setting.

A polynomial relational class of binary CSP

Finding a solution to a constraint satisfaction problem (CSP) is known to be an NP-hard task. Considerable effort has been spent on identifying tractable classes of CSP, in other words, classes of constraint satisfaction problems for which there are polynomial time recognition and resolution algorithms. In this article, we present a relational tractable class of binary CSP. Our key contribution is a new ternary operation that we name mjx. We first characterize mjx-closed relations which leads to an optimal algorithm to recognize such relations. To reduce space and time complexity, we define a new storage technique for these relations which reduces the complexity of establishing a form of strong directional path consistency, the consistency level that solves all instances of the proposed class (and, indeed, of all relational classes closed under a majority polymorphism).

Semantic Optimization in Tractable Classes of Conjunctive Queries

This paper reports on recent advances in semantic query optimization. We focus on the core class of conjunctive queries (CQs). Since CQ evaluation is NP-complete, a long line of research has concentrated on identifying fragments of CQs that can be efficiently evaluated. One of the most general such restrictions corresponds to bounded generalized hypertreewidth, which extends the notion of acyclicity. Here we discuss the problem of reformulating a CQ into one of bounded generalized hypertreewidth. Furthermore, we study whether knowing that such a reformulation exists alleviates the cost of CQ evaluation. In case a CQ cannot be reformulated as one of bounded generalized hypertreewidth, we discuss how it can be approximated in an optimal way. All the above issues are examined both for the constraint-free case, and the case where constraints, in fact, tuple-generating and equality-generating dependencies, are present

A kinetic view of GPCR allostery and biased agonism.

G-protein-coupled receptors (GPCRs) are one of the most tractable classes of drug targets. These dynamic proteins can adopt multiple active states that are linked to distinct functional outcomes. Such states can be differentially stabilized by ligands interacting with the endogenous agonist-binding orthosteric site and/or by ligands acting via spatially distinct allosteric sites, leading to the phenomena of 'biased agonism' or 'biased modulation'. These paradigms are having a major impact on modern drug discovery, but it is becoming increasingly apparent that 'kinetic context', at the level of both ligand-receptor and receptor-signal pathway kinetics, can have a profound impact on the observation and quantification of these phenomena. The concept of kinetic context thus represents an important new consideration that should be routinely incorporated into contemporary chemical biology and drug discovery studies of GPCR bias and allostery.

Discovering Archipelagos of Tractability for Constraint Satisfaction and Counting

The Constraint Satisfaction Problem (CSP) is a central and generic computational problem which provides a common framework for many theoretical and practical applications. A central line of research is concerned with the identification of classes of instances for which CSP can be solved in polynomial time; such classes are often called “islands of tractability.” A prominent way of defining islands of tractability for CSP is to restrict the relations that may occur in the constraints to a fixed set, called a constraint language , whereas a constraint language is conservative if it contains all unary relations. Schaefer’s famous Dichotomy Theorem (STOC 1978) identifies all islands of tractability in terms of tractable constraint languages over a Boolean domain of values. Since then, many extensions and generalizations of this result have been obtained. Recently, Bulatov (TOCL 2011, JACM 2013) gave a full characterization of all islands of tractability for CSP and the counting version #CSP that are defined in terms of conservative constraint languages. This article addresses the general limit of the mentioned tractability results for CSP and #CSP, that they only apply to instances where all constraints belong to a single tractable language (in general, the union of two tractable languages is not tractable). We show that we can overcome this limitation as long as we keep some control of how constraints over the various considered tractable languages interact with each other. For this purpose, we utilize the notion of a strong backdoor of a CSP instance, as introduced by Williams et al. (IJCAI 2003), which is a set of variables that when instantiated, moves the instance to an island of tractability, that is, to a tractable class of instances. We consider strong backdoors into scattered classes , consisting of CSP instances where each connected component belongs entirely to some class from a list of tractable classes. Figuratively speaking, a scattered class constitutes an archipelago of tractability . The main difficulty lies in finding a strong backdoor of given size k ; once it is found, we can try all possible instantiations of the backdoor variables and apply the polynomial time algorithms associated with the islands of tractability on the list component-wise. Our main result is an algorithm that, given a CSP instance with n variables, finds in time f ( k ) n O (1) a strong backdoor into a scattered class (associated with a list of finite conservative constraint languages) of size k or correctly decides that there is not such a backdoor. This also gives the running time for solving (#)CSP, provided that (#)CSP is polynomial-time tractable for the considered constraint languages. Our result makes significant progress towards the main goal of the backdoor-based approach to CSPs—the identification of maximal base classes for which small backdoors can be detected efficiently.

A BTP-Based Family of Variable Elimination Rules for Binary CSPs

The study of broken-triangles is becoming increasingly ambitious, by both solving constraint satisfaction problems (CSPs) in polynomial time and reducing search space size through value merging or variable elimination. Considerable progress has been made in extending this important concept, such as dual broken-triangle and weakly broken-triangle, in order to maximize the number of captured tractable CSP instances and/or the number of merged values. Specifically, m-wBTP allows to merge more values than BTP. k-BTP, WBTP and m-BTP permit to capture more tractable instances than BTP. Here, we introduce a new weaker form of BTP, which will be called m-fBTP for flexible broken-triangle property. m-fBTP allows on the one hand to eliminate more variables than BTP while preserving satisfiability and on the other to define new bigger tractable class for which arc consistency is a decision procedure. Likewise, m-fBTP permits to merge more values than BTP but less than m-wBTP.

Discrete Time Term Structure Theory and Consistent Recalibration Models

We develop a theory and applications of forward characteristic processes in discrete time following the ideas of a seminal paper of Kallsen and Kruhner [Finance Stoch., 19 (2015), pp. 583--615]. Particular emphasis is given to the dynamics of volatility surfaces which can be readily formulated and implemented from our chosen discrete point of view. In mathematical terms we provide an algorithmic answer to the following question: describe a rich, still tractable class of discrete time stochastic processes, whose marginal distributions are given at an initial time and which are free of arbitrage. In terms of mathematical finance we can construct models with predescribed (implied) volatility surface at initial time and a quite general volatility surface dynamics along time. In terms of the works of Carmona and Nadtochiy [Finance Stoch., 13 (2009), pp. 1--48; Finance Stoch., 16 (2012), pp. 63--104] we analyze the dynamics of tangent affine models. We believe that the discrete approach due to its technical sim...

The Power of Local Consistency in Conjunctive Queries and Constraint Satisfaction Problems

Answering conjunctive queries is a fundamental problem in database theory, and it is equivalent to solving constraint satisfaction problems in artificial intelligence and to other fundamental problems arising in computer science, which can be recast in terms of looking for homomorphisms between relational structures. The problem is NP-hard, so that several research efforts have been made in the literature for identifying tractable classes, known as islands of tractability, as well as for devising clever heuristics for solving efficiently real-world instances. Many heuristic approaches are based on enforcing on the given instance a property called local consistency (also, relational arc-consistency), where each tuple in every query atom matches at least one tuple in every other query atom. Interestingly, for many well-known classes of instances, such as for the acyclic ones, enforcing local consistency is even sufficient to solve the given instance correctly. However, the precise power of such a procedure was unclear, but for some very restricted cases. The paper provides answers to long-standing questions about the precise power of algorithms based on enforcing local consistency. The paper deals with both the general framework of tree projections, where local consistency is enforced among arbitrary views defined over the given database instance, and the specific cases where such views are computed according to the so-called structural decomposition methods, such as generalized hypertree width, component hypertree decompositions, and so on. Moreover, the paper deals with both decision and computation problems, by characterizing those tuples that are correct projections of query answers, which finds application in algorithms for answering queries and solving constraint satisfaction problems. As a relevant special case, the power of algorithms based on enforcing local consistency is characterized over the fundamental and deeply studied class of acyclic conjunctive queries. It turns out that local consistency provides the correct answer to a Boolean acyclic query if, and only if, the query is semantically acyclic.

A hidden Markov model for latent temporal clustering with application to ideological alignment in the U.S. Supreme Court

An alternative approach to modeling latent time-varying sequences of clusters demonstrates certain benefits over existing methods for analyzing Supreme Court voting data. The family of Markov chains presented here satisfies important statistical properties, including exchangeability, consistency under subsampling, and reversibility with respect to a tractable class of two-parameter partition models. These properties make the model robust to missing data, choice of labels, and changes in the sample over time. When combined with an appropriate model for the response, exchangeability and consistency give rise to the stronger model properties of label equivariance and non-interference, respectively, which together make inferences readily interpretable. These and other aspects of the approach are illustrated with a detailed analysis of voting data in the Supreme Court over the period 1946–2012. The results of this analysis agree with what is known about the Court’s behavior during this period of time. In some cases, this approach detects aspects of the Court that other quantitative analyses, such as Martin–Quinn scores, do not.

Multiproduct Pricing Made Simple

We study pricing by multiproduct firms in the context of unregulated monopoly, regulated monopoly and Cournot oligopoly. Using the concept of consumer surplus as a function of quantities (rather than prices), we present simple formulas for optimal prices and show that Cournot equilibrium exists and corresponds to a Ramsey optimum. We then present a tractable class of demand systems that involve a generalized form of homothetic preferences. As well as standard homothetic preferences, this class includes linear and logit demand. Within the class, profit-maximizing quantities are proportional to efficient quantities. We discuss cost-passthrough, including cases where optimal prices do not depend on other products' costs. Finally, we discuss optimal monopoly regulation when the firm has private information about its vector of marginal costs, and show that if the probability distribution over costs satisfies an independence property, then optimal regulation leaves relative price decisions to the firm.

Tractable classes for CSPs of arbitrary arity: from theory to practice

The research of this thesis focuses on the analysis of polynomial classes and their practical exploitation for solving constraint satisfaction problems (CSPs) with finite domains. In particular, I worked on bridging the gap between theoretical works and practical results in constraint solvers. Specifically, the goal of this thesis is to find explanation for the effectiveness of solvers, and also to show that studied tractable classes are not artificial since several real-problems among the ones used in the CSP 2008 Competition belong to them. Our work is organized into three main parts. In the first part, we proposed several types of microstructures for CSPs of arbitrary arity which are based on some knwon binary encoding of non-binary CSPs like, dual encoding, hidden-variable transformation and mixed (or double) encoding. These theoretical tools are designed to facilitate the study of tractable classes, sets of CSP instances which can be solved in polytime, when the constraints are non-binary. After that, we propose a new tractable classes of CSPs whose the highlighting should allow on the one hand to explain the effectiveness of solvers of the state of the art namely FC, MAC, RFL and on the second hand to provide the opportunities for easy integration in these solvers. These would include the definition of new tractable classes without using of an ad hoc algorithms as in the traditional case. These new tractable classes are related to the number of maximal cliques in the microstructure of binary or non-binary CSP. In the last part, we focus on the presence of instances belonging to polynomial classes in classical benchmarks used by the CP community. We study in particular the Broken-Triangle Property (BTP) and its extension DBTP to CSP of arbitrary arity. Next, we prove that BTP can also be used to reduce the size of the search space by merging pairs of values on which no broken triangle exists. Finally, we introduce a formal framework, called transformation, and we develop the concept of hidden tractable class that we exploit from an experimental point of view.

Robust stability analysis with cycling-based LPTV scaling: part I. Fundamental results on its relationship with lifting-based LPTV scaling

ABSTRACTThis paper is concerned with robust stability analysis of discrete-time linear periodically time-varying (LPTV) systems using the cycling-based LPTV scaling approach. To study the properties of this approach in comparison with the lifting-based LPTV scaling approach, we consider exploiting the framework of representing the associated robust stability conditions with infinite matrices. Since it serves as a common framework for comparing the two different LPTV scaling approaches, it provides us with new insights into the relationship between the cycling-based and lifting-based scaling approaches. In particular, we derive fundamental results that enable us to reduce the comparison, with respect to conservativeness in robust stability analysis, of the two scaling approaches with restricted and tractable classes of separators to a modified comparison of the associated classes of what we call infinite-dimensional separators arising in the above infinite matrix framework.

Robust stability analysis with cycling-based LPTV scaling. Part II: its properties under the use of FIR separators

ABSTRACTThis paper is concerned with robust stability analysis of discrete-time linear periodically time-varying (LPTV) systems using the cycling-based LPTV scaling approach. It consists of applying the separator-type robust stability theorem through the cycling-based treatment of such systems, where this paper aims at revealing fundamental properties of this approach when we confine ourselves to what we call finite impulse response (FIR) separators as a theoretically and numerically very tractable class of separators. Specifically, we clarify such properties of the cycling-based LPTV scaling approach using FIR separators that cannot readily be seen under the treatment of general class of separators. This is accomplished by comparing it with another approach, called lifting-based LPTV scaling using FIR separators, through the framework of representing the associated robust stability conditions with infinite matrices. More precisely, this leads us to clarifying the fundamental relationships between the cycling-based and lifting-based approaches under the use of FIR separators. We also provide a numerical example demonstrating the fundamental relationships clarified in this paper.