Concentration inequalities are standard lemmas providing upper bounds on deviations of random variables. To formalize concentration inequalities, we have been developing a general library of lemmas for probability theory in the Rocq prover. This effort led us to revisit already established technical aspects of the Mathematical Components libraries. In this paper, we report on improvements of general interest resulting from our formalization. We devise types for numeric values and a lightweight semi-decision procedure, based on interval arithmetic. We also extend the hierarchy of available mathematical structures to formalize Lebesgue spaces. We illustrate our new formalization of probability theory with the complete proof of a concentration inequality for Bernoulli sampling.

Abstract One can perform equational reasoning about computational effects with a purely functional programming language thanks to monads. Even though equational reasoning for effectful programs is desirable, it is not yet mainstream. This is partly because it is difficult to maintain pencil-and-paper proofs of large examples. We propose a formalization of a hierarchy of effects using monads in the Coq proof assistant that makes monadic equational reasoning practical. Our main idea is to formalize the hierarchy of effects and algebraic laws as interfaces like it is done when formalizing hierarchy of algebras in dependent-type theory. Thanks to this approach, we clearly separate equational laws from models. We can then take advantage of the sophisticated rewriting capabilities of Coq and build libraries of lemmas to achieve concise proofs of programs. We can also use the resulting framework to leverage on Coq’s mathematical theories and formalize models of monads. In this article, we explain how we formalize a rich hierarchy of effects (nondeterminism, state, probability, etc.), how we mechanize examples of monadic equational reasoning from the literature, and how we apply our framework to the design of equational laws for a subset of ML with references.

For performance and verification in machine learning, new methods have recently been proposed that optimise learning systems to satisfy formally expressed logical properties. Among these methods, differentiable logics (DLs) are used to translate propositional or first-order formulae into loss functions deployed for optimisation in machine learning. At the same time, recent attempts to give programming language support for verification of neural networks showed that DLs can be used to compile verification properties to machine-learning backends. This situation is calling for stronger guarantees about the soundness of such compilers, the soundness and compositionality of DLs, and the differentiability and performance of the resulting loss functions. In this paper, we propose an approach to formalise existing DLs using the Mathematical Components library in the Coq proof assistant. Thanks to this formalisation, we are able to give uniform semantics to otherwise disparate DLs, give formal proofs to existing informal arguments, find errors in previous work, and provide formal proofs to missing conjectured properties. This work is meant as a stepping stone for the development of programming language support for verification of machine learning.

Probabilistic programming languages are used to write probabilistic models to make probabilistic inferences. A number of rigorous semantics have recently been proposed that are now available to carry out formal verification of probabilistic programs. In this paper, we extend an existing formalization of measure and integration theory with s-finite kernels, a mathematical structure to interpret typing judgments in the semantics of a probabilistic programming language. The resulting library makes it possible to reason formally about transformations of probabilistic programs and their execution.

The algebraic properties of the combination of probabilistic choice and nondeterministic choice have long been a research topic in program semantics. This paper explains a formalization in the Coq proof assistant of a monad equipped with both choices: the geometrically convex monad. This formalization has an immediate application: it provides a model for a monad that implements a non-trivial interface which allows for proofs by equational reasoning using probabilistic and nondeterministic effects. We explain the technical choices we made to go from the literature to a complete Coq formalization, from which we identify reusable theories about mathematical structures such as convex spaces and concrete categories, and that we integrate in a framework for monadic equational reasoning.

Succinct data structures give space-efficient representations of large amounts of data without sacrificing performance. They rely on cleverly designed data representations and algorithms. We present here the formalization in Coq/SSReflect of two different tree-based succinct representations and their accompanying algorithms. One is the Level-Order Unary Degree Sequence, which encodes the structure of a tree in breadth-first order as a sequence of bits, where access operations can be defined in terms of Rank and Select, which work in constant time for static bit sequences. The other represents dynamic bit sequences as binary balanced trees, where Rank and Select present a low logarithmic overhead compared to their static versions, and with efficient insertion and deletion. The two can be stacked to provide a dynamic representation of dictionaries for instance. While both representations are well-known, we believe this to be their first formalization and a needed step towards provably-safe implementations of big data.

Because of the increasing complexity of mathematical proofs, there is a growing interest in formalization using proof-assistants. In this paper, we explain new formal proofs of standard lemmas in data compression (Jensen's and Kraft's inequalities) as well as concrete applications (to the analysis of compression methods and Shannon-Fano codes). We explain in particular how one turns the paper proof into formal terms and the relation between the informal proof and the formal one. These formalizations come as an extension to an existing formal library for information theory and error-correcting codes.

We are interested in the formal specification of safety properties of robot manipulators down to the mathematical physics. To this end, we have been developing a formalization of the mathematics of rigid body transformations in the Coq proof-assistant. It can be used to address the forward kinematics problem, i.e., the computation of the position and orientation of the end-effector of a robot manipulator in terms of the link and joint parameters. Our formalization starts by extending the Mathematical Components library with a new theory for angles and by developing three-dimensional geometry. We use these theories to formalize the foundations of robotics. First, we formalize a comprehensive theory of three-dimensional rotations, including exponentials of skew-symmetric matrices and quaternions. Then, we provide a formalization of the various representations of rigid body transformations: isometries, homogeneous representation, the Denavit-Hartenberg convention, and screw motions. These ingredients make it possible to formalize robot manipulators: we illustrate this aspect by an application to the SCARA robot manipulator.