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Functional and logic programming : 14th International Symposium, FLOPS 2018, Nagoya, Japan, May 9-11, 2018, Proceedings / edited by John P. Gallagher, Martin Sulzmann.

This book constitutes the proceedings of the 14th International Symposium on Functional and Logic Programming, FLOPS 2018, held in Nagoya, Japan, in May 2018. The 17 papers presented in this volume were carefully reviewed and selected from 41 submissions. They cover all aspects of the design, semant...

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Bibliographic Details
Online Access: Full Text (via Springer)
Corporate Author: Fuji International Symposium on Functional and Logic Programming Nagoya, Japan
Other Authors: Gallagher, John P. (Editor), Sulzmann, Martin (Editor)
Other title:FLOPS 2018.
Format: Conference Proceeding eBook
Language:English
Published: Cham : Springer, 2018.
Series:Lecture notes in computer science ; 10818.
Subjects:
Functional programming (Computer science) > Congresses.
Logic programming > Congresses.
Computer programming.
Functional programming (Computer science)
Logic programming.
Programming languages (Electronic computers)
Software engineering.
Electronic book.
Conference papers and proceedings.
Table of Contents:
  • Intro
  • Preface
  • Organization
  • Abstracts of Invited Talks
  • Can Programming Be Liberated from Unidirectional Style?
  • miniKanren: A Family of Languages for Relational Programming
  • Building Verified Cryptographic Components Using F*
  • Contents
  • Formal Verification of the Correspondence Between Call-by-Need and Call-by-Name
  • 1 Introduction
  • 2 -Calculus and Call-by-Name Evaluation
  • 3 Ariola and Felleisen's Call-by-Need -Calculus
  • 4 Outline of Our Standardization-Based Proof
  • 5 Formalization in Coq
  • 6 Related Work
  • 7 Conclusion
  • References.
  • Direct Encodings of NP-Complete Problems into Horn Sequents of Multiplicative Linear Logic
  • 1 Introduction
  • 2 Intuitionistic Multiplicative Linear Logic, Horn Sequents, and MLL
  • 2.1 Intuitionistic Multiplicative Linear Logic and Horn Sequents
  • 2.2 Multiplicative Linear Logic
  • 2.3 MLL Proof Nets
  • 3 The Encoding of 3D MATCHING
  • 3.1 Preliminaries
  • 3.2 The Encoding into a Horn Sequent
  • 3.3 The Correctness Proof
  • 4 The Encoding of PARTITION
  • 4.1 Preliminaries
  • 4.2 The Encoding into a Horn Sequent
  • 4.3 The Correctness Proof
  • 5 Concluding Remarks
  • References.
  • To SKI, Semantically
  • 1 Introduction
  • 2 Lambda- and SKI-calculi and the Bracket Abstraction
  • 3 Semantic Translation
  • 3.1 Lazy Weakening
  • 4 OCaml Implementation
  • 4.1 The Eta-Optimization
  • 5 Compiling Real Programs
  • 6 Linear algorithm
  • 7 Related Work
  • 8 Conclusions
  • References
  • Program Extraction for Mutable Arrays
  • 1 Introduction
  • 2 Finite Types and Finite Functions in Coq
  • 2.1 A Finite Type Library-fintype
  • 2.2 A Finite Function Library-finfun
  • 3 Representing Mutable Arrays in Coq
  • 3.1 Program Extraction for the Array State Monad.
  • 3.2 Small Example: Swap Two Elements
  • 4 Optimizations by an Improved Extraction Plugin
  • 4.1 Destructing Large Records
  • 4.2 -expansion on Case Analysis
  • 5 Case Studies
  • 5.1 The Union-find Data Structure
  • 5.2 The Quicksort Algorithm
  • 6 Related Work
  • 7 Conclusion
  • References
  • Functional Pearl: Folding Polynomials of Polynomials
  • 1 Introduction
  • 2 Univariate and Multivariate Polynomials
  • 2.1 Univariate Polynomial and Its Semantics
  • 2.2 Bivariate Polynomials
  • 2.3 Multivariate Polynomials
  • 3 Operations on Polynomials
  • 3.1 Rotation
  • 3.2 Substitution
  • 3.3 Expansion.
  • 4 Compiling Polynomials
  • 5 Conclusions and Related Work
  • References
  • A Functional Perspective on Machine Learning via Programmable Induction and Abduction
  • 1 A Principled Functional Language for Machine Learning
  • 2 Deduction, Induction, Abduction
  • 2.1 Proofs-as-Programs for Induction
  • 2.2 Proofs-as-Programs for Abduction
  • 3 Programming with Induction-Abduction
  • 4 The Abductive Calculus
  • 5 DecML, A Functional Language for Machine Learning
  • 6 Related and Further Work
  • References
  • Polymorphic Rewrite Rules: Confluence, Type Inference, and Instance Validation.

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