1,176 research outputs found
First Class Call Stacks: Exploring Head Reduction
Weak-head normalization is inconsistent with functional extensionality in the
call-by-name -calculus. We explore this problem from a new angle via
the conflict between extensionality and effects. Leveraging ideas from work on
the -calculus with control, we derive and justify alternative
operational semantics and a sequence of abstract machines for performing head
reduction. Head reduction avoids the problems with weak-head reduction and
extensionality, while our operational semantics and associated abstract
machines show us how to retain weak-head reduction's ease of implementation.Comment: In Proceedings WoC 2015, arXiv:1606.0583
Beyond Polarity: Towards a Multi-Discipline Intermediate Language with Sharing
The study of polarity in computation has revealed that an "ideal" programming language combines both call-by-value and call-by-name evaluation; the two calling conventions are each ideal for half the types in a programming language. But this binary choice leaves out call-by-need which is used in practice to implement lazy-by-default languages like Haskell. We show how the notion of polarity can be extended beyond the value/name dichotomy to include call-by-need by only adding a mechanism for sharing and the extra polarity shifts to connect them, which is enough to compile a Haskell-like functional language with user-defined types
Compiling With Classical Connectives
The study of polarity in computation has revealed that an "ideal" programming
language combines both call-by-value and call-by-name evaluation; the two
calling conventions are each ideal for half the types in a programming
language. But this binary choice leaves out call-by-need which is used in
practice to implement lazy-by-default languages like Haskell. We show how the
notion of polarity can be extended beyond the value/name dichotomy to include
call-by-need by adding a mechanism for sharing which is enough to compile a
Haskell-like functional language with user-defined types. The key to capturing
sharing in this mixed-evaluation setting is to generalize the usual notion of
polarity "shifts:" rather than just two shifts (between positive and negative)
we have a family of four dual shifts.
We expand on this idea of logical duality -- "and" is dual to "or;" proof is
dual to refutation -- for the purpose of compiling a variety of types. Based on
a general notion of data and codata, we show how classical connectives can be
used to encode a wide range of built-in and user-defined types. In contrast
with an intuitionistic logic corresponding to pure functional programming,
these classical connectives bring more of the pleasant symmetries of classical
logic to the computationally-relevant, constructive setting. In particular, an
involutive pair of negations bridges the gulf between the wide-spread notions
of parametric polymorphism and abstract data types in programming languages. To
complete the study of duality in compilation, we also consider the dual to
call-by-need evaluation, which shares the computation within the control flow
of a program instead of computation within the information flow
Lambda Calculus with Explicit Recursion
AbstractThis paper is concerned with the study ofλ-calculus with explicit recursion, namely of cyclicλ-graphs. The starting point is to treat aλ-graph as a system of recursion equations involvingλ-terms and to manipulate such systems in an unrestricted manner, using equational logic, just as is possible for first-order term rewriting. Surprisingly, now the confluence property breaks down in an essential way. Confluence can be restored by introducing a restraining mechanism on the substitution operation. This leads to a family ofλ-graph calculi, which can be seen as an extension of the family ofλσ-calculi (λ-calculi with explicit substitution). While theλσ-calculi treat the let-construct as a first-class citizen, our calculi support the letrec, a feature that is essential to reason about time and space behavior of functional languages and also about compilation and optimizations of program
Application of Statistical Learning Control to the Design of a Fixed-Order Controller for a Flexible Beam
This paper shows how probabilistic methods and statistical learning theory can provide approximate solutions to “difficult” control problems. The paper also introduces bootstrap learning methods to drastically reduce the bound on the number of samples required to achieve a performance level. These results are then applied to obtain more efficient algorithms which probabilistically guarantee stability and robustness levels when designing controllers for uncertain systems. The paper includes examples of the applications of these methods
Statistical learning control of delay systems: theory and algorithms
Recently, probabilistic methods and statistical learning theory have been shown to provide approximate solutions to “difficult” control problems. Unfortunately, the number of samples required in order to guarantee stringent performance levels may be prohibitively large. In this paper, using recent results by the authors, a more efficient statistical algorithm is presented. Using this algorithm we design static output controllers for a nonlinear plant with uncertain delay
Statistical-learning control of an ABR explicit rate algorithm for ATM switches
Illustrates the application of statistical-learning control results for the design of an available bit rate (ABR) congestion control algorithm. The proposed methodology allows us to take into account the nonlinearities of the model and the uncertainty of the parameters in the design phase. Some simulation results are shown
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