scholarly journals An Abstract Decision Procedure for Satisfiability in the Theory of Recursive Data Types

2007 ◽  
Vol 174 (8) ◽  
pp. 23-37 ◽  
Author(s):  
Clark Barrett ◽  
Igor Shikanian ◽  
Cesare Tinelli
Keyword(s):  
Decision Procedure ◽  
Data Types ◽  
Recursive Data

scholarly journals A Brief Introduction to the PVS2C Code Generator

10.29007/v7k2 ◽  
2018 ◽  
Author(s):  
Natarajan Shankar
Keyword(s):  
Higher Order ◽  
Specification Language ◽  
Functional Language ◽  
Data Types ◽  
Code Generator ◽  
Numeric Data ◽  
Recursive Data

We present a brief tutorial on the PVS2C code generator for producing C code from an applicative fragment of the PVS specification language. This fragment roughly corresponds to a self-contained functional language. The tutorial covers the generation of C code for numeric data types and associated operations, arrays, recursive data types, and higher- order operations.

scholarly journals An Abstract Decision Procedure for a Theory of Inductive Data Types

2007 ◽  
Vol 3 (1-2) ◽  
pp. 21-46 ◽  
Author(s):  
Clark Barrett ◽  
Igor Shikanian ◽  
Cesare Tinelli
Keyword(s):  
Decision Procedure ◽  
Data Types

Partially strict non-recursive data types

1993 ◽  
Vol 3 (2) ◽  
pp. 191-215 ◽  
Author(s):  
Eric Nöcker ◽  
Sjaak Smetsers
Keyword(s):  
Data Type ◽  
Functional Languages ◽  
Data Types ◽  
Composite Object ◽  
Space And Time ◽  
Input And Output ◽  
The Individual ◽  
Recursive Data

AbstractValues belonging to lazy data types have the advantage that sub-components can be accessed without evaluating the values as a whole: unneeded components remain unevaluated. A disadvantage is that often a large amount of space and time is required to handle lazy data types properly. Many special constructor cells are needed to ‘glue’ the individual parts of a composite object together and to store it in the heap. We present a way of representing data in functional languages which makes these special constructor cells superfluous. In some cases, no heap at all is needed to store this data. To make this possible, we introduce a new kind of data type: (partially) strict non-recursive data types. The main advantage of these types is that an efficient call-by-value mechanism can be used to pass arguments. A restrictive subclass of (partially) strict non-recursive data types, partially strict tuples, is treated more comprehensively. We also give examples of important classes of applications. In particular, we show how partially strict tuples can be used to define very efficient input and output primitives. Measurements of applications written in Concurrent Clean which exploit partially strict tuples have shown that speedups of 2 to 3 times are reasonable. Moreover, much less heap space is required when partially strict tuples are used.

Pattern minimization problems over recursive data types

2008 ◽  
Vol 43 (9) ◽  
pp. 267-274
Author(s):  
Alexander Krauss
Keyword(s):  
Data Types ◽  
Minimization Problems ◽  
Recursive Data

On the Power of Model Theory in Specifying Abstract Data Types and in Capturing Their Recursiveness

1983 ◽  
Vol 6 (2) ◽  
pp. 127-170
Author(s):  
Alberto Bertoni ◽  
Giancarlo Mauri ◽  
Pierangelo Miglioli
Keyword(s):  
Model Theory ◽  
Point Of View ◽  
Necessary Condition ◽  
Abstract Data Type ◽  
Abstract Data Types ◽  
Data Types ◽  
Abstract Data ◽  
Definition Of ◽  
Recursive Data ◽  
Better Than

In this paper a comparative analysis of some algebraic and model-theoretic tools to specify abstract data types is presented: our aim is to show that, in order to capture a quite relevant feature such as the recursiveness of abstract data types, Model Theory works better than Category Theory. To do so, we analyze various notions such as “initiality”, “finality”, “monoinitiality”, “epifinality”, “weak monoinitiality” and “weak epifinality”, both from the point of view of “abstractness” and of “cardinality”, in a general model theoretical frame. For the second aspect, it is shown that only “initiality”, “monoinitiality”, “epifinality” and “weak epifinality” allow us to select countable models (for theories with a countable language), a necessary condition to get recursive data types, while this is not the case for “finality” and “weak monoinitiality”. An extensive analysis is then devoted to the problem of the recursiveness of abstract data types: we provide a formal definition of recursiveness and show that it neither collapses, nor it is incompatible with the “abstractness” requirement. We also show that none of the above quoted categorial notions captures recursiveness. Finally, we consider our own definition of “abstract data type”, based on typically model-theoretic notions, and illustrate the sense according to which it captures recursiveness.

scholarly journals Oblivious algebraic data types

2022 ◽  
Vol 6 (POPL) ◽  
pp. 1-29
Author(s):  
Qianchuan Ye ◽  
Benjamin Delaware
Keyword(s):  
Programming Languages ◽  
Type System ◽  
Secure Computation ◽  
Data Types ◽  
Sensitive Data ◽  
Private Data ◽  
Algebraic Data Types ◽  
Recursive Data Structures ◽  
Recursive Data ◽  
Cryptographic Techniques

Secure computation allows multiple parties to compute joint functions over private data without leaking any sensitive data, typically using powerful cryptographic techniques. Writing secure applications using these techniques directly can be challenging, resulting in the development of several programming languages and compilers that aim to make secure computation accessible. Unfortunately, many of these languages either lack or have limited support for rich recursive data structures, like trees. In this paper, we propose a novel representation of structured data types, which we call oblivious algebraic data types, and a language for writing secure computations using them. This language combines dependent types with constructs for oblivious computation, and provides a security-type system which ensures that adversaries can learn nothing more than the result of a computation. Using this language, authors can write a single function over private data, and then easily build an equivalent secure computation according to a desired public view of their data.

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