Learning-assisted Theorem Proving with Millions of Lemmas

Large formal mathematical libraries consist of millions of atomic inference steps that give rise to a corresponding number of proved statements (lemmas). Analogously to the informal mathematical practice, only a tiny fraction of such statements is named and re-used in later proofs by formal mathematicians. In this work, we suggest and implement criteria defining the estimated usefulness of the HOL Light lemmas for proving further theorems. We use these criteria to mine the large inference graph of the lemmas in the HOL Light and Flyspeck libraries, adding up to millions of the best lemmas to the pool of statements that can be re-used in later proofs. We show that in combination with learning-based relevance filtering, such methods significantly strengthen automated theorem proving of new conjectures over large formal mathematical libraries such as Flyspeck.Comment: journal version of arXiv:1310.2797 (which was submitted to LPAR conference

Improving legibility of natural deduction proofs is not trivial

In formal proof checking environments such as Mizar it is not merely the validity of mathematical formulas that is evaluated in the process of adoption to the body of accepted formalizations, but also the readability of the proofs that witness validity. As in case of computer programs, such proof scripts may sometimes be more and sometimes be less readable. To better understand the notion of readability of formal proofs, and to assess and improve their readability, we propose in this paper a method of improving proof readability based on Behaghel's First Law of sentence structure. Our method maximizes the number of local references to the directly preceding statement in a proof linearisation. It is shown that our optimization method is NP-complete.Comment: 33 page

Singular Gaussian Measures in Detection Theory

No abstract availabl

About the possibility of minimal blow up for Navier-Stokes solutions with data in H˙s(R3)\dot{H}^s(R^3)

Considering initial data in $\dot{H}^s$, with \frac{1}{2} \textless{} s \textless{} \frac{3}{2}, this paper is devoted to the study of possible blowing-up Navier-Stokes solutions such that (T*(u\_{0}) -t)^{\frac{1}{2} (s- \frac{1}{2})} \,\, \| u \|\_{\dot{H}^s}} is bounded. Our result is in the spirit of the tremendous works of L. Escauriaza, G. Seregin, and V. $\breve{\mathrm{S}}$ver$\acute{\mathrm{a}}$k and I. Gallagher, G. Koch, F. Planchon, where they proved there is no blowing-up solution which remain bounded in $L^3(R^3)$. The main idea is that if such blowing-up solutions exist, they satisfy critical properties

Description Length Based Signal Detection in singular Spectrum Analysis

This paper provides an information theoretic analysis of the signal-noise separation problem in Singular Spectrum Analysis. We present a signal-plus-noise model based on the Karhunen-Loève expansion and use this model to motivate the construction of a minimum description length criterion that can be employed to select both the window length and the signal. We show that under very general regularity conditions the criterion will identify the true signal dimension with probability one as the sample size increases, and will choose the smallest window length consistent with the Whitney embedding theorem. Empirical results obtained using simulated and real world data sets indicate that the asymptotic theory is reflected in observed behaviour, even in relatively small samples.Karhunen-Loève expansion, minimum description length, signal-plus-noise model, Singular Spectrum Analysis, embedding