We show that approximating the shortest vector problem (in any $\ell_p$ norm) to within any constant factor less than $\sqrt[p]2$ is hard for NP under reverse unfaithful random reductions with inverse polynomial error probability. In particular, approximating the shortest vector problem is not in RP (random polynomial time), unless NP equals RP. We also prove a proper NP-hardness result (i.e., hardness under deterministic many-one reductions) under a reasonable number theoretic conjecture on the distribution of square-free smooth numbers. As part of our proof, we give an alternative construction of Ajtai's constructive variant of Sauer's lemma that greatly simplifies Ajtai's original proof.

The Shortest Vector in a Lattice is Hard to Approximate to within Some Constant.

Dutertre and de Moura developed a simplex-based solver for linear rational arithmetic that has an incremental interface and provides unsatisfiable cores. We present a verification of their algorithm in Isabelle/HOL that significantly extends previous work by Spasic and Maric. Based on the simplex algorithm we further formalize Farkas’ Lemma. With this result we verify that linear rational constraints are satisfiable over $\mathbb {Q}$ if and only they are satisfiable over $\mathbb {R}$. Hence, our verified simplex algorithm is also able to decide satisfiability in linear real arithmetic.

/pdf/verifying-an-incremental-theory-solver-for-linear-arithmetic-5csyaihtwo.pdf

Verifying an Incremental Theory Solver for Linear Arithmetic in Isabelle/HOL

We formally verify the Berlekamp–Zassenhaus algorithm for factoring square-free integer polynomials in Isabelle/HOL. We further adapt an existing formalization of Yun’s square-free factorization algorithm to integer polynomials, and thus provide an efficient and certified factorization algorithm for arbitrary univariate polynomials. The algorithm first performs factorization in the prime field $$\mathrm {GF}(p){}$$ and then performs computations in the ring of integers modulo $$p^k$$, where both p and k are determined at runtime. Since a natural modeling of these structures via dependent types is not possible in Isabelle/HOL, we formalize the whole algorithm using locales and local type definitions. Through experiments we verify that our algorithm factors polynomials of degree up to 500 within seconds.

/pdf/a-verified-implementation-of-the-berlekamp-zassenhaus-8skppt49qx.pdf

A Verified Implementation of the Berlekamp-Zassenhaus Factorization Algorithm.

I develop a formal framework for propositional satifisfiability with the conflict-driven clause learning (CDCL) procedure using the Isabelle/HOL proof assistant. The framework offers a convenient way to prove metatheorems and experiment with variants, including the Davis-Putnam-Logemann-Loveland procedure. The most noteworthy aspects of my work are the inclusion of rules for forget and restart and the refinement approach.

I use the formalization to develop three extensions: First, an incremental solving extension of CDCL. Second, I verify an optimizing CDCL (OCDCL): Given a cost function on literals, OCDCL derives an optimal model with minimum cost. Finally, I work on model covering. Thanks to the CDCL framework I can reuse, these extensions are easier to develop.

Through a chain of refinements, I connect the abstract CDCL calculus first to a more concrete calculus, then to a SAT solver expressed in a simple functional programming language, and finally to a SAT solver in an imperative language, with total correctness guarantees. The imperative version relies on the two-watched-literal data structure and other optimizations found in modern solvers. I used the Isabelle Refinement Framework to automate the most tedious refinement steps. After that, I extend this work with further optimizations like blocking literals and the use of machine words as long as possible, before switching to unbounded integers to keep completeness.

/pdf/formalization-of-logical-calculi-in-isabelle-hol-1u22m8i4om.pdf

Formalization of Logical Calculi in Isabelle/HOL

We present the first formal verification of approximation algorithms for NP-complete optimization problems: vertex cover, independent set, load balancing, and bin packing. We uncover incompletenesses in existing proofs and improve the approximation ratio in one case.

/pdf/verified-approximation-algorithms-3efqdsk0jd.pdf

Verified Approximation Algorithms

The LLL basis reduction algorithm was the first polynomial-time algorithm to compute a reduced basis of a given lattice, and hence also a short vector in the lattice. It thereby approximately solves an NP-hard problem. The algorithm has several applications in number theory, computer algebra and cryptography. Recently, the first mechanized soundness proof of the LLL algorithm has been developed in Isabelle/HOL. However, this proof did not include a formal statement of the algorithm’s complexity. Furthermore, the resulting implementation was inefficient in practice. We address both of these shortcomings in this paper. First, we prove the correctness of a more efficient implementation of the LLL algorithm that uses only integer computations. Second, we formally prove statements on the polynomial running-time.

/pdf/a-verified-efficient-implementation-of-the-lll-basis-4bvevxex9f.pdf

A Verified Efficient Implementation of the LLL Basis Reduction Algorithm

We implement a decision procedure for linear mixed integer arithmetic and formally verify its soundness in Isabelle/HOL. We further integrate this procedure into one application, namely into CeTA, a formally verified certifier to check untrusted termination proofs. This checking involves assertions of unsatisfiability of linear integer inequalities; previously, only a sufficient criterion for such checks was supported. To verify the soundness of the decision procedure, we first formalize the proof that every satisfiable set of linear integer inequalities also has a small solution, and give explicit upper bounds. To this end we mechanize several important theorems on linear programming, including statements on integrality and bounds. The procedure itself is then implemented as a branch-and-bound algorithm, and is available in several languages via Isabelle’s code generator. It internally relies upon an adapted version of an existing verified incremental simplex algorithm.

/pdf/verifying-a-solver-for-linear-mixed-integer-arithmetic-in-4z50yr0lai.pdf

Verifying a Solver for Linear Mixed Integer Arithmetic in Isabelle/HOL

We formalize a proof of Motzkin’s transposition theorem and Farkas’ lemma in Isabelle/HOL. Our proof is based on the formalization of the simplex algorithm which, given a set of linear constraints, either returns a satisfying assignment to the problem or detects unsatisfiability. By reusing facts about the simplex algorithm we show that a set of linear constraints is unsatisfiable if and only if there is a linear combination of the constraints which evaluates to a trivially unsatisfiable inequality.

Farkas' Lemma and Motzkin's Transposition Theorem.

The LLL basis reduction algorithm was the first polynomial-time algorithm to compute a reduced basis of a given lattice, and hence also a short vector in the lattice. It approximates an NP-hard problem where the approximation quality solely depends on the dimension of the lattice, but not the lattice itself. The algorithm has applications in number theory, computer algebra and cryptography. In this paper, we provide an implementation of the LLL algorithm. Both its soundness and its polynomial running-time have been verified using Isabelle/HOL. Our implementation is nearly as fast as an implementation in a commercial computer algebra system, and its efficiency can be further increased by connecting it with fast untrusted lattice reduction algorithms and certifying their output. We additionally integrate one application of LLL, namely a verified factorization algorithm for univariate integer polynomials which runs in polynomial time.

/pdf/formalizing-the-lll-basis-reduction-algorithm-and-the-lll-3kzvrg84ez.pdf

Formalizing the LLL Basis Reduction Algorithm and the LLL Factorization Algorithm in Isabelle/HOL

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