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Type and effect systems are a tool to analyse statically the behaviour of programs with effects. We present a proof based on the so called reducibility candidates that a suitable stratification of the type and effect system entails the…

Logic in Computer Science · Computer Science 2010-07-01 Roberto Amadio

Combining the approaches made in works with Galeotti and Passmann, we define and study a notion of "almost sure" realizability with parameter-free ordinal Turing machines (OTMs). In particular, we show that, in contrast to the classical…

Logic · Mathematics 2026-05-19 Merlin Carl

The idea of iterative process optimization based on collected output measurements, or "real-time optimization" (RTO), has gained much prominence in recent decades, with many RTO algorithms being proposed, researched, and developed. While…

Optimization and Control · Mathematics 2013-08-29 Gene A. Bunin , Grégory François , Dominique Bonvin

Multicriteria adjustable robust optimization (MARO) problems arise in a wide variety of practical settings, for example, in the design of a building's energy supply. However, no general approaches, neither for the characterization of…

Optimization and Control · Mathematics 2024-06-13 Elisabeth Halser , Elisabeth Finhold , Neele Leithäuser , Jan Schwientek , Katrin Teichert , Karl-Heinz Küfer

The field of implicit complexity has recently produced several bounded-complexity programming languages. This kind of language allows to implement exactly the functions belonging to a certain complexity class. We here present a…

Logic in Computer Science · Computer Science 2012-06-22 Aloïs Brunel , Antoine Madet

An adaptive parametric reduced-order modeling method based on interpolating poles of reduced-order models is proposed in this paper. To guarantee correct interpolation, a pole-matching process is conducted to determine which poles of two…

Numerical Analysis · Mathematics 2019-08-05 Yao Yue , Lihong Feng , Peter Benner

Provability logic GLP is well-known to be incomplete w.r.t. Kripke semantics. A natural topological semantics of GLP interprets modalities as derivative operators of a polytopological space. Such spaces satisfying all the axioms of GLP are…

Logic · Mathematics 2016-02-19 Lev D. Beklemishev , David Gabelaia

We introduce a notion of tractability for ill-posed operator equations in Hilbert space. For such operator equations the asymptotics of the best possible rate of reconstruction in terms of the underlying noise level is known in many cases.…

Numerical Analysis · Mathematics 2024-05-07 Peter Mathé , Bernd Hofmann

This is the first of a series of papers in which we study deep computations (ultracomputations) and deep iterates, formalizing the ideas of "asymptotic limit" of computations and compositional iterates, respectively. In this first paper of…

Logic · Mathematics 2026-01-28 Samson Alva , Eduardo Dueñez , Jose Iovino , Claire Walton

We present realizability and realization logic, two program logics that jointly address the problem of finding solutions in semantics-guided synthesis. What is new is that we proceed eagerly and not only analyze a single candidate program…

Logic in Computer Science · Computer Science 2024-03-12 Roland Meyer , Jakob Tepe , Sebastian Wolff

We investigate completeness and parametricity for a general class of realizability semantics for System F defined in terms of closure operators over sets of $\lambda$-terms. This class includes most semantics used for normalization…

Logic in Computer Science · Computer Science 2023-06-22 Paolo Pistone

When can a model of a physical system be regarded as computable? We provide the definition of a computable physical model to answer this question. The connection between our definition and Kreisel's notion of a mechanistic theory is…

Logic · Mathematics 2013-08-09 Matthew P. Szudzik

This paper focuses on optimizing probabilities of events of interest defined over general controlled discrete-time Markov processes. It is shown that the optimization over a wide class of $\omega$-regular properties can be reduced to the…

Probability · Mathematics 2014-07-22 Ilya Tkachev , Alexandru Mereacre , Joost-Pieter Katoen , Alessandro Abate

Rule-based explanations provide simple reasons explaining the behavior of machine learning classifiers at given points in the feature space. Several recent methods (Anchors, LORE, etc.) purport to generate rule-based explanations for…

Machine Learning · Computer Science 2023-01-24 Brett Mullins

We give a theoretical and applicable framework for dealing with real-world phenomena. Joining pointwise and pointfree notions in BISH, natural topology gives a faithful idea of important concepts and results in intuitionism. Natural…

Logic · Mathematics 2012-10-24 Frank Waaldijk

In this paper we investigate the tractability of robust Markov Decision Processes (RMDPs) under various structural assumptions on the uncertainty set. Surprisingly, we show that in all generality (i.e. without any assumption on the…

Optimization and Control · Mathematics 2024-11-14 Julien Grand-Clément , Nian Si , Shengbo Wang

System requirements related to concepts like information flow, knowledge, and robustness cannot be judged in terms of individual system executions, but rather require an analysis of the relationship between multiple executions. Such…

Logic in Computer Science · Computer Science 2025-01-15 Bernd Finkbeiner

We introduce an axiomatization for the notion of computation. Based on the idea of Brouwer choice sequences, we construct a model, denoted by $E$, which satisfies our axioms and $E \models \mathrm{ P \neq NP}$. In other words, regarding…

Computational Complexity · Computer Science 2020-01-22 Rasoul Ramezanian

Recent studies have shown that the majority of published computational models in systems biology and physiology are not repeatable or reproducible. There are a variety of reasons for this. One of the most likely reasons is that given how…

Other Quantitative Biology · Quantitative Biology 2021-07-13 Herbert M. Sauro

When predictive models are used to support complex and important decisions, the ability to explain a model's reasoning can increase trust, expose hidden biases, and reduce vulnerability to adversarial attacks. However, attempts at…

Machine Learning · Computer Science 2019-07-11 Dimitris Bertsimas , Arthur Delarue , Patrick Jaillet , Sebastien Martin