From Exponential to Polynomial Complexity: Efficient Permutation Counting with Subword Constraints
- URL: http://arxiv.org/abs/2411.16744v1
- Date: Sat, 23 Nov 2024 19:52:11 GMT
- Title: From Exponential to Polynomial Complexity: Efficient Permutation Counting with Subword Constraints
- Authors: Martin Mathew, Javier Noda,
- Abstract summary: Counting distinct permutations with replacement, especially when involving multiple subwords, is a longstanding challenge in analysis.
This paper introduces a novel framework that presents closed-form formulas for calculating distinct permutations with replacement.
We then extend our foundational formula to handle multiple subwords through the development of an additional formula.
- Score: 0.0
- License:
- Abstract: Counting distinct permutations with replacement, especially when involving multiple subwords, is a longstanding challenge in combinatorial analysis, with critical applications in cryptography, bioinformatics, and statistical modeling. This paper introduces a novel framework that presents closed-form formulas for calculating distinct permutations with replacement, fundamentally reducing the time complexity from exponential to linear relative to the sequence length for single-subword calculations. We then extend our foundational formula to handle multiple subwords through the development of an additional formula. Unlike traditional methods relying on brute-force enumeration or recursive algorithms, our approach leverages novel combinatorial constructs and advanced mathematical techniques to achieve unprecedented efficiency. This comprehensive advancement in reducing computational complexity not only simplifies permutation counting but also establishes a new benchmark for scalability and versatility. We also demonstrate the practical utility of our formulas through diverse applications, including the simultaneous identification of multiple genetic motifs in DNA sequences and complex pattern analysis in cryptographic systems, using a computer program that runs the proposed formulae.
Related papers
- A novel framework for systematic propositional formula simplification based on existential graphs [1.104960878651584]
This paper presents a novel simplification calculus for propositional logic derived from Peirce's existential graphs' rules of inference and implication graphs.
Our rules can be applied to propositional logic formulae in nested form, are equivalence-preserving, guarantee a monotonically decreasing number of variables, clauses and literals, and maximise the preservation of structural problem information.
arXiv Detail & Related papers (2024-05-27T11:42:46Z) - Tractable Bounding of Counterfactual Queries by Knowledge Compilation [51.47174989680976]
We discuss the problem of bounding partially identifiable queries, such as counterfactuals, in Pearlian structural causal models.
A recently proposed iterated EM scheme yields an inner approximation of those bounds by sampling the initialisation parameters.
We show how a single symbolic knowledge compilation allows us to obtain the circuit structure with symbolic parameters to be replaced by their actual values.
arXiv Detail & Related papers (2023-10-05T07:10:40Z) - An Efficient Algorithm for Clustered Multi-Task Compressive Sensing [60.70532293880842]
Clustered multi-task compressive sensing is a hierarchical model that solves multiple compressive sensing tasks.
The existing inference algorithm for this model is computationally expensive and does not scale well in high dimensions.
We propose a new algorithm that substantially accelerates model inference by avoiding the need to explicitly compute these covariance matrices.
arXiv Detail & Related papers (2023-09-30T15:57:14Z) - A Hybrid System for Systematic Generalization in Simple Arithmetic
Problems [70.91780996370326]
We propose a hybrid system capable of solving arithmetic problems that require compositional and systematic reasoning over sequences of symbols.
We show that the proposed system can accurately solve nested arithmetical expressions even when trained only on a subset including the simplest cases.
arXiv Detail & Related papers (2023-06-29T18:35:41Z) - A Recursively Recurrent Neural Network (R2N2) Architecture for Learning
Iterative Algorithms [64.3064050603721]
We generalize Runge-Kutta neural network to a recurrent neural network (R2N2) superstructure for the design of customized iterative algorithms.
We demonstrate that regular training of the weight parameters inside the proposed superstructure on input/output data of various computational problem classes yields similar iterations to Krylov solvers for linear equation systems, Newton-Krylov solvers for nonlinear equation systems, and Runge-Kutta solvers for ordinary differential equations.
arXiv Detail & Related papers (2022-11-22T16:30:33Z) - Set Interdependence Transformer: Set-to-Sequence Neural Networks for
Permutation Learning and Structure Prediction [6.396288020763144]
Set-to-sequence problems occur in natural language processing, computer vision and structure prediction.
Previous attention-based methods require $n$ layers of their set transformations to explicitly represent $n$-th order relations.
We propose a novel neural set encoding method called the Set Interdependence Transformer, capable of relating the set's permutation invariant representation to its elements within sets of any cardinality.
arXiv Detail & Related papers (2022-06-08T07:46:49Z) - Permutation Invariant Representations with Applications to Graph Deep
Learning [8.403227482145297]
This paper presents two Euclidean embeddings of quotient space generated by matrices that are identified modulo arbitrary row permutations.
An almost everywhere injective scheme can be implemented with minimal redundancy and low computational cost.
arXiv Detail & Related papers (2022-03-14T23:13:59Z) - High-Dimensional Sparse Bayesian Learning without Covariance Matrices [66.60078365202867]
We introduce a new inference scheme that avoids explicit construction of the covariance matrix.
Our approach couples a little-known diagonal estimation result from numerical linear algebra with the conjugate gradient algorithm.
On several simulations, our method scales better than existing approaches in computation time and memory.
arXiv Detail & Related papers (2022-02-25T16:35:26Z) - Dynamic programming by polymorphic semiring algebraic shortcut fusion [1.9405875431318445]
Dynamic programming (DP) is an algorithmic design paradigm for the efficient, exact solution of intractable, problems.
This paper presents a rigorous algebraic formalism for systematically deriving DP algorithms, based on semiring.
arXiv Detail & Related papers (2021-07-05T00:51:02Z) - Analyzing the Nuances of Transformers' Polynomial Simplification
Abilities [11.552059052724907]
We show that Transformers consistently struggle with numeric multiplication.
We explore two ways to mitigate this: Learning Curriculum and a Symbolic Calculator approach.
Both approaches provide significant gains over the vanilla Transformers-based baseline.
arXiv Detail & Related papers (2021-04-29T03:52:46Z) - Isometric Multi-Shape Matching [50.86135294068138]
Finding correspondences between shapes is a fundamental problem in computer vision and graphics.
While isometries are often studied in shape correspondence problems, they have not been considered explicitly in the multi-matching setting.
We present a suitable optimisation algorithm for solving our formulation and provide a convergence and complexity analysis.
arXiv Detail & Related papers (2020-12-04T15:58:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.