1,495 research outputs found
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
Computing with Classical Real Numbers
There are two incompatible Coq libraries that have a theory of the real
numbers; the Coq standard library gives an axiomatic treatment of classical
real numbers, while the CoRN library from Nijmegen defines constructively valid
real numbers. Unfortunately, this means results about one structure cannot
easily be used in the other structure. We present a way interfacing these two
libraries by showing that their real number structures are isomorphic assuming
the classical axioms already present in the standard library reals. This allows
us to use O'Connor's decision procedure for solving ground inequalities present
in CoRN to solve inequalities about the reals from the Coq standard library,
and it allows theorems from the Coq standard library to apply to problem about
the CoRN reals
Superposition as a logical glue
The typical mathematical language systematically exploits notational and
logical abuses whose resolution requires not just the knowledge of domain
specific notation and conventions, but not trivial skills in the given
mathematical discipline. A large part of this background knowledge is expressed
in form of equalities and isomorphisms, allowing mathematicians to freely move
between different incarnations of the same entity without even mentioning the
transformation. Providing ITP-systems with similar capabilities seems to be a
major way to improve their intelligence, and to ease the communication between
the user and the machine. The present paper discusses our experience of
integration of a superposition calculus within the Matita interactive prover,
providing in particular a very flexible, "smart" application tactic, and a
simple, innovative approach to automation.Comment: In Proceedings TYPES 2009, arXiv:1103.311
Formal Proofs for Nonlinear Optimization
We present a formally verified global optimization framework. Given a
semialgebraic or transcendental function and a compact semialgebraic domain
, we use the nonlinear maxplus template approximation algorithm to provide a
certified lower bound of over . This method allows to bound in a modular
way some of the constituents of by suprema of quadratic forms with a well
chosen curvature. Thus, we reduce the initial goal to a hierarchy of
semialgebraic optimization problems, solved by sums of squares relaxations. Our
implementation tool interleaves semialgebraic approximations with sums of
squares witnesses to form certificates. It is interfaced with Coq and thus
benefits from the trusted arithmetic available inside the proof assistant. This
feature is used to produce, from the certificates, both valid underestimators
and lower bounds for each approximated constituent. The application range for
such a tool is widespread; for instance Hales' proof of Kepler's conjecture
yields thousands of multivariate transcendental inequalities. We illustrate the
performance of our formal framework on some of these inequalities as well as on
examples from the global optimization literature.Comment: 24 pages, 2 figures, 3 table
Learning-Assisted Automated Reasoning with Flyspeck
The considerable mathematical knowledge encoded by the Flyspeck project is
combined with external automated theorem provers (ATPs) and machine-learning
premise selection methods trained on the proofs, producing an AI system capable
of answering a wide range of mathematical queries automatically. The
performance of this architecture is evaluated in a bootstrapping scenario
emulating the development of Flyspeck from axioms to the last theorem, each
time using only the previous theorems and proofs. It is shown that 39% of the
14185 theorems could be proved in a push-button mode (without any high-level
advice and user interaction) in 30 seconds of real time on a fourteen-CPU
workstation. The necessary work involves: (i) an implementation of sound
translations of the HOL Light logic to ATP formalisms: untyped first-order,
polymorphic typed first-order, and typed higher-order, (ii) export of the
dependency information from HOL Light and ATP proofs for the machine learners,
and (iii) choice of suitable representations and methods for learning from
previous proofs, and their integration as advisors with HOL Light. This work is
described and discussed here, and an initial analysis of the body of proofs
that were found fully automatically is provided
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