12,562 research outputs found

    A Verified Certificate Checker for Finite-Precision Error Bounds in Coq and HOL4

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    Being able to soundly estimate roundoff errors of finite-precision computations is important for many applications in embedded systems and scientific computing. Due to the discrepancy between continuous reals and discrete finite-precision values, automated static analysis tools are highly valuable to estimate roundoff errors. The results, however, are only as correct as the implementations of the static analysis tools. This paper presents a formally verified and modular tool which fully automatically checks the correctness of finite-precision roundoff error bounds encoded in a certificate. We present implementations of certificate generation and checking for both Coq and HOL4 and evaluate it on a number of examples from the literature. The experiments use both in-logic evaluation of Coq and HOL4, and execution of extracted code outside of the logics: we benchmark Coq extracted unverified OCaml code and a CakeML-generated verified binary

    Certified Roundoff Error Bounds Using Semidefinite Programming.

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    Roundoff errors cannot be avoided when implementing numerical programs with finite precision. The ability to reason about rounding is especially important if one wants to explore a range of potential representations, for instance for FPGAs or custom hardware implementation. This problem becomes challenging when the program does not employ solely linear operations as non-linearities are inherent to many interesting computational problems in real-world applications. Existing solutions to reasoning are limited in the presence of nonlinear correlations between variables, leading to either imprecise bounds or high analysis time. Furthermore, while it is easy to implement a straightforward method such as interval arithmetic, sophisticated techniques are less straightforward to implement in a formal setting. Thus there is a need for methods which output certificates that can be formally validated inside a proof assistant. We present a framework to provide upper bounds on absolute roundoff errors. This framework is based on optimization techniques employing semidefinite programming and sums of squares certificates, which can be formally checked inside the Coq theorem prover. Our tool covers a wide range of nonlinear programs, including polynomials and transcendental operations as well as conditional statements. We illustrate the efficiency and precision of this tool on non-trivial programs coming from biology, optimization and space control. Our tool produces more precise error bounds for 37 percent of all programs and yields better performance in 73 percent of all programs

    On Sound Relative Error Bounds for Floating-Point Arithmetic

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    State-of-the-art static analysis tools for verifying finite-precision code compute worst-case absolute error bounds on numerical errors. These are, however, often not a good estimate of accuracy as they do not take into account the magnitude of the computed values. Relative errors, which compute errors relative to the value's magnitude, are thus preferable. While today's tools do report relative error bounds, these are merely computed via absolute errors and thus not necessarily tight or more informative. Furthermore, whenever the computed value is close to zero on part of the domain, the tools do not report any relative error estimate at all. Surprisingly, the quality of relative error bounds computed by today's tools has not been systematically studied or reported to date. In this paper, we investigate how state-of-the-art static techniques for computing sound absolute error bounds can be used, extended and combined for the computation of relative errors. Our experiments on a standard benchmark set show that computing relative errors directly, as opposed to via absolute errors, is often beneficial and can provide error estimates up to six orders of magnitude tighter, i.e. more accurate. We also show that interval subdivision, another commonly used technique to reduce over-approximations, has less benefit when computing relative errors directly, but it can help to alleviate the effects of the inherent issue of relative error estimates close to zero

    Certifying floating-point implementations using Gappa

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    High confidence in floating-point programs requires proving numerical properties of final and intermediate values. One may need to guarantee that a value stays within some range, or that the error relative to some ideal value is well bounded. Such work may require several lines of proof for each line of code, and will usually be broken by the smallest change to the code (e.g. for maintenance or optimization purpose). Certifying these programs by hand is therefore very tedious and error-prone. This article discusses the use of the Gappa proof assistant in this context. Gappa has two main advantages over previous approaches: Its input format is very close to the actual C code to validate, and it automates error evaluation and propagation using interval arithmetic. Besides, it can be used to incrementally prove complex mathematical properties pertaining to the C code. Yet it does not require any specific knowledge about automatic theorem proving, and thus is accessible to a wide community. Moreover, Gappa may generate a formal proof of the results that can be checked independently by a lower-level proof assistant like Coq, hence providing an even higher confidence in the certification of the numerical code. The article demonstrates the use of this tool on a real-size example, an elementary function with correctly rounded output

    Cryptographic techniques used to provide integrity of digital content in long-term storage

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    The main objective of the project was to obtain advanced mathematical methods to guarantee the verification that a required level of data integrity is maintained in long-term storage. The secondary objective was to provide methods for the evaluation of data loss and recovery. Additionally, we have provided the following initial constraints for the problem: a limitation of additional storage space, a minimal threshold for desired level of data integrity and a defined probability of a single-bit corruption. With regard to the main objective, the study group focused on the exploration methods based on hash values. It has been indicated that in the case of tight constraints, suggested by PWPW, it is not possible to provide any method based only on the hash values. This observation stems from the fact that the high probability of bit corruption leads to unacceptably large number of broken hashes, which in turn stands in contradiction with the limitation for additional storage space. However, having loosened the initial constraints to some extent, the study group has proposed two methods that use only the hash values. The first method, based on a simple scheme of data subdivision in disjoint subsets, has been provided as a benchmark for other methods discussed in this report. The second method ("hypercube" method), introduced as a type of the wider class of clever-subdivision methods, is built on the concept of rewriting data-stream into a n-dimensional hypercube and calculating hash values for some particular (overlapping) sections of the cube. We have obtained interesting results by combining hash value methods with error-correction techniques. The proposed framework, based on the BCH codes, appears to have promising properties, hence further research in this field is strongly recommended. As a part of the report we have also presented features of secret sharing methods for the benefit of novel distributed data-storage scenarios. We have provided an overview of some interesting aspects of secret sharing techniques and several examples of possible applications
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