Tuning as convex optimisation: a polynomial tuner for multi-parametric combinatorial samplers

Combinatorial samplers are algorithmic schemes devised for the approximate- and exact-size generation of large random combinatorial structures, such as context-free words, various tree-like data structures, maps, tilings, RNA molecules. They can be adapted to combinatorial specifications with additional parameters, allowing for a more flexible control over the output profile of parametrised combinatorial patterns. One can control, for instance, the number of leaves, profile of node degrees in trees or the number of certain sub-patterns in generated strings. However, such a flexible control requires an additional and nontrivial tuning procedure. Using techniques of convex optimisation, we present an efficient tuning algorithm for multi-parametric combinatorial specifications. Our algorithm works in polynomial time in the system description length, the number of tuning parameters, the number of combinatorial classes in the specification, and the logarithm of the total target size. We demonstrate the effectiveness of our method on a series of practical examples, including rational, algebraic, and so-called Pólya specifications. We show how our method can be adapted to a broad range of less typical combinatorial constructions, including symmetric polynomials, labelled sets and cycles with cardinality lower bounds, simple increasing trees or substitutions. Finally, we discuss some practical aspects of our prototype tuner implementation and provide its benchmark results.

Alexander polynomial and spanning trees

Inspired by the combinatorial constructions in earlier work of the authors that generalized the classical Alexander polynomial to a large class of spatial graphs with a balanced weight on edges, we show that the value of the Alexander polynomial evaluated at [Formula: see text] gives the weighted number of the spanning trees of the graph.

Combinatorial Constructions Of Ordered Orthogonal Arrays & Ordered Covering Arrays

In this thesis, we consider combinatorial objects called ordered orthogonal arrays, which are related to orthogonal arrays and Latin squares. We also introduce a new combinatorial method to the construction of these objects, as well as developing new ones. We discuss the applications of ordered orthogonal arrays and ordered covering arrays, which generalize covering arrays. We adapt existing combinatorial methods to the construction of these objects, as well as developing new ones. We discuss the applications of ordered orthogonal arrays and ordered covering arrays to quasi-Monte Carlo integration through the construction of point sets called (t,m,s)-nets and a new object we call (t,m,s)-covering nets.

Combinatorial Constructions Of Ordered Orthogonal Arrays & Ordered Covering Arrays

In this thesis, we consider combinatorial objects called ordered orthogonal arrays, which are related to orthogonal arrays and Latin squares. We also introduce a new combinatorial method to the construction of these objects, as well as developing new ones. We discuss the applications of ordered orthogonal arrays and ordered covering arrays, which generalize covering arrays. We adapt existing combinatorial methods to the construction of these objects, as well as developing new ones. We discuss the applications of ordered orthogonal arrays and ordered covering arrays to quasi-Monte Carlo integration through the construction of point sets called (t,m,s)-nets and a new object we call (t,m,s)-covering nets.

COMPARATIVE ANALYSIS OF MONOLITHIC AND CYCLIC NOISE-PROTECTIVE CODES EFFECTIVENESS

Comparative analysis of the effectiveness of monolithic and cyclic noise protective codes built on "Ideal Ring Bundles" (IRBs) as the common theoretical basis for synthesis, researches and application of the codes for improving technical indexes of coding systems with respect to performance, reliability, transformation speed, and security has been realized. IRBs are cyclic sequences of positive integers, which form perfect partitions of a finite interval of integers. Sums of connected IRB elements enumerate the natural integers set exactly R-times. The IRB-codes both monolithic and cyclic ones forming on the underlying combinatorial constructions can be used for finding optimal solutions for configure of an applicable coding systems based on the common mathematical platform. The mathematical model of noise-protective data coding systems presents remarkable properties of harmonious developing real space. These properties allow configure codes with useful possibilities. First of them belong to the self-correcting codes due to monolithic arranged both symbols "1" and of course "0" of each allowed codeword. This allows you to automatically detect and correct errors by the monolithic structure of the encoded words. IRB codes of the second type provide improving noise protection of the codes by choosing the optimal ratio of information parameters. As a result of comparative analysis of cyclic IRB-codes based with optimized parameters and monolithic IRB-codes, it was found that optimized cyclic IRB codes have an advantage over monolithic in relation to a clearly fixed number of detected and corrected codes, while monolithic codes favorably differ in the speed of message decoding due to their inherent properties of self-correction and encryption. Monolithic code characterized by packing of the same name characters in the form of solid blocks. The latter are capable of encoding data on several levels at the same time, which expands the ability to encrypt and protect encoded data from unauthorized access. Evaluation of the effectiveness of coding optimization methods by speed of formation of coding systems, method power, and error correcting has been made. The model based on the combinatorial configurations contemporary theory, which can find a wide scientific field for the development of fundamental and applied researches into information technolodies, including application multidimensional models, as well as algorithms for synthesis of the underlying models.

Reduced Density Matrix Cumulants: The Combinatorics of Size-Consistency and Generalized Normal Ordering

Reduced density matrix cumulants play key roles in the theory of both reduced density matrices and multiconfigurational normal ordering, but the underlying formalism has remained mysterious. We present a new, simpler generating function for reduced density matrix cumulants that is formally identical to equating the coupled cluster and configuration interaction ansätze. This is shown to be a general mechanism to convert between a multiplicatively separable quantity and an additively separable quantity, as defined by a set of axioms. It is shown that both the cumulants of probability theory and reduced density matrices are entirely combinatorial constructions, where the differences can be associated to changes in the notion of "multiplicative separability'' for expectation values of random variables compared to reduced density matrices. We compare our generating function to that of previous works and criticize previous claims of probabilistic significance of the reduced density matrix cumulants. Finally, we present the simplest proof to date of the Generalized Normal Ordering formalism to explore the role of reduced density matrix cumulants therein.

Reduced Density Matrix Cumulants: The Combinatorics of Size-Consistency and Generalized Normal Ordering

Reduced density matrix cumulants play key roles in the theory of both reduced density matrices and multiconfigurational normal ordering, but the underlying formalism has remained mysterious. We present a new, simpler generating function for reduced density matrix cumulants that is formally identical to equating the coupled cluster and configuration interaction ansätze. This is shown to be a general mechanism to convert between a multiplicatively separable quantity and an additively separable quantity, as defined by a set of axioms. It is shown that both the cumulants of probability theory and reduced density matrices are entirely combinatorial constructions, where the differences can be associated to changes in the notion of "multiplicative separability'' for expectation values of random variables compared to reduced density matrices. We compare our generating function to that of previous works and criticize previous claims of probabilistic significance of the reduced density matrix cumulants. Finally, we present the simplest proof to date of the Generalized Normal Ordering formalism to explore the role of reduced density matrix cumulants therein.