How Fast Can We Play Tetris Greedily With Rectangular Pieces?

Consider a variant of Tetris played on a board of width $w$ and infinite height, where the pieces are axis-aligned rectangles of arbitrary integer dimensions, the pieces can only be moved before letting them drop, and a row does not disappear once it is full. Suppose we want to follow a greedy strategy: let each rectangle fall where it will end up the lowest given the current state of the board. To do so, we want a data structure which can always suggest a greedy move. In other words, we want a data structure which maintains a set of $O(n)$ rectangles, supports queries which return where to drop the rectangle, and updates which insert a rectangle dropped at a certain position and return the height of the highest point in the updated set of rectangles. We show via a reduction to the Multiphase problem [P\u{a}tra\c{s}cu, 2010] that on a board of width $w=\Theta(n)$, if the OMv conjecture [Henzinger et al., 2015] is true, then both operations cannot be supported in time $O(n^{1/2-\epsilon})$ simultaneously. The reduction also implies polynomial bounds from the 3-SUM conjecture and the APSP conjecture. On the other hand, we show that there is a data structure supporting both operations in $O(n^{1/2}\log^{3/2}n)$ time on boards of width $n^{O(1)}$, matching the lower bound up to a $n^{o(1)}$ factor.Comment: Correction of typos and other minor correction

Proceedings of the 2022 XCSP3 Competition

This document represents the proceedings of the 2022 XCSP3 Competition. The results of this competition of constraint solvers were presented at FLOC (Federated Logic Conference) 2022 Olympic Games, held in Haifa, Israel from 31th July 2022 to 7th August, 2022.Comment: arXiv admin note: text overlap with arXiv:1901.0183

Proof-relevant resolution : the foundations of constructive proof automation

Dependent type theory is an expressive programming language. This language allows to write programs that carry proofs of their properties. This in turn gives high confidence in such programs, making the software trustworthy. Yet, the trustworthiness comes for a price: type inference involves an increasing number of proof obligations. Automation of this process becomes necessary for any system with dependent types that aims to be usable in practice. At the same time, implementation of automation in a verified manner is prohibitively complex. Sometimes, external solvers are used to aid the automation. These solvers may be based on classical logic and may not be themselves verified, thus compromising the guarantees provided by constructive nature of type theory. In this thesis, we explore the idea of proof relevant resolution that allows automation of type inference in type theory in a verifiable and constructive manner, hence to restore the confidence in programs and the trustworthiness of software. Technical content of this thesis is threefold. First, we propose a novel framework for proof-relevant resolution. We take two constructive logics, Horn-clause and hereditary Harrop formulae logics as a starting point. We formulate the standard big-step operational semantics of these logics. We expose their Curry-Howard nature by treating formulae of these logics as types and proofs as terms thus developing a theory of proof-relevant resolution. We develop small-step operational semantics of proof-relevant resolution and prove it sound with respect to the big-step operational semantics. Secondly, we demonstrate our approach on an example of type inference in Logical Framework (LF). We translate a type-inference problem in LF into resolution in proof-relevant Horn-clause logic. Such resolution provides, besides an answer substitution to logic variables, a proof term that captures the resolution tree. We interpret the proof term as a derivation of well-formedness judgement of the object in the original problem. This allows for a straightforward implementation of type checking of the resolved solution since type checking is reduced to verifying the derivation captured by the proof term. The theoretical development is substantiated by an implementation. Finally, we demonstrate that our approach allows to reason about semantic properties of code. Type class resolution has been well-known to be a proof-relevant fragment of Horn-clause logic, and recently its coinductive extensions were introduced. In this thesis, we show that all of these extensions amalgamate with the theoretical framework we introduce. Our novel result here is exposing that the coinductive extensions are actually based on hereditary Harrop logic, rather than Horn-clause logic. We establish a number of soundness and completeness results for them. We also discuss soundness of program transformation that are allowed by proof-relevant presentation of type class resolution

Random Testing For Language Design

Property-based random testing can facilitate formal verification, exposing errors early on in the proving process and guiding users towards correct specifications and implementations. However, effective random testing often requires users to write custom generators for well-distributed random data satisfying complex logical predicates, a task which can be tedious and error prone. In this work, I aim to reduce the cost of property-based testing by making such generators easier to write, read and maintain. I present a domain-specific language, called Luck, in which generators are conveniently expressed by decorating predicates with lightweight annotations to control both the distribution of generated values and the amount of constraint solving that happens before each variable is instantiated. I also aim to increase the applicability of testing to formal verification by bringing advanced random testing techniques to the Coq proof assistant. I describe QuickChick, a QuickCheck clone for Coq, and improve it by incorporating ideas explored in the context of Luck to automatically derive provably correct generators for data constrained by inductive relations. Finally, I evaluate both QuickChick and Luck in a variety of complex case studies from programming languages literature, such as information-flow abstract machines and type systems for lambda calculi

Automated Deduction – CADE 28

This open access book constitutes the proceeding of the 28th International Conference on Automated Deduction, CADE 28, held virtually in July 2021. The 29 full papers and 7 system descriptions presented together with 2 invited papers were carefully reviewed and selected from 76 submissions. CADE is the major forum for the presentation of research in all aspects of automated deduction, including foundations, applications, implementations, and practical experience. The papers are organized in the following topics: Logical foundations; theory and principles; implementation and application; ATP and AI; and system descriptions

Automated Reasoning

This volume, LNAI 13385, constitutes the refereed proceedings of the 11th International Joint Conference on Automated Reasoning, IJCAR 2022, held in Haifa, Israel, in August 2022. The 32 full research papers and 9 short papers presented together with two invited talks were carefully reviewed and selected from 85 submissions. The papers focus on the following topics: Satisfiability, SMT Solving,Arithmetic; Calculi and Orderings; Knowledge Representation and Jutsification; Choices, Invariance, Substitutions and Formalization; Modal Logics; Proofs System and Proofs Search; Evolution, Termination and Decision Prolems. This is an open access book