arXiv subjects

Mohammad Abdulaziz

Publications and source records attributed to Mohammad Abdulaziz.

16 recordsLinked to original sources

arXiv2026

A Formal Analysis of Capacity Scaling Algorithms for Minimum-Cost Flows

We present formalisations of the correctness of executable algorithms to solve minimum-cost flow problems in Isabelle/HOL. Two of the algorithms are based on the technique of scaling, most notably Orlin's algorithm, which has the fastest known running time for solving the problem of minimum-cost flow. We also include a formalisation of the worst-case running time argument for Orlin's algorithm. Our verified implementation of this algorithm, which is derived by the technique of stepwise refinement, is fully executable and was integrated into a reusable formal library on graph algorithms. Because the problems for which Orlin's algorithm works are restricted, we also verified an executable reduction from the general minimum-cost flow problem. We believe we are the first to formally consider the problem of minimum-cost flows and, more generally, any scaling algorithms. Our work has also led to a number of mathematical insights and improvements to proofs as well as theorem statements, compared to all existing expositions.

cs.LO↗

arXiv2026

Just Type It in Isabelle! AI Agents Drafting, Mechanizing, and Generalizing from Human Hints

Type annotations are essential when printing terms in a way that preserves their meaning under reparsing and type inference. We study the problem of complete and minimal type annotations for rank-one polymorphic $λ$-calculus terms, as used in Isabelle. Building on prior work by Smolka, Blanchette et al., we give a metatheoretical account of the problem, with a full formal specification and proofs, and formalize it in Isabelle/HOL. Our development is a series of experiments featuring human-driven and AI-driven formalization workflows: a human and an LLM-powered AI agent independently produce pen-and-paper proofs, and the AI agent autoformalizes both in Isabelle, with further human-hinted AI interventions refining and generalizing the development.

cs.LO↗

arXiv2026

Formal Primal-Dual Algorithm Analysis

We present an ongoing effort to build a framework and a library in Isabelle/HOL for formalising primal-dual arguments for the analysis of algorithms. We discuss a number of example formalisations from the theory of matching algorithms, covering classical algorithms like the Hungarian Method, widely considered the first primal-dual algorithm, and modern algorithms like the Adwords algorithm, which models the assignment of search queries to advertisers in the context of search engines.

cs.LO↗

arXiv2025

A Formal Correctness Proof of Edmonds' Blossom Shrinking Algorithm

We present the first formal correctness proof of Edmonds' blossom shrinking algorithm for maximum cardinality matching in general graphs. We focus on formalising the mathematical structures and properties that allow the algorithm to run in worst-case polynomial running time. We formalise Berge's lemma, blossoms and their properties, and a mathematical model of the algorithm, showing that it is totally correct. We provide the first detailed proofs of many of the facts underlying the algorithm's correctness.

cs.LO↗

arXiv2025

Formally Verified Certification of Unsolvability of Temporal Planning Problems

We present an approach to unsolvability certification of temporal planning. Our approach is based on encoding the planning problem into a network of timed automata, and then using an efficient model checker on the network followed by a certificate checker to certify the output of the model checker. Our approach prioritises trustworthiness of the certification: we formally verify our implementation of the encoding to timed automata using the theorem prover Isabelle/HOL and we use an existing certificate checker (also formally verified in Isabelle/HOL) to certify the model checking result.

cs.LO↗

arXiv2025

A Formally Verified IEEE 754 Floating-Point Implementation of Interval Iteration for MDPs

We present an efficiently executable, formally verified implementation of interval iteration for MDPs. Our correctness proofs span the entire development from the high-level abstract semantics of MDPs to a low-level implementation in LLVM that is based on floating-point arithmetic. We use the Isabelle/HOL proof assistant to verify convergence of our abstract definition of interval iteration and employ step-wise refinement to derive an efficient implementation in LLVM code. To that end, we extend the Isabelle Refinement Framework with support for reasoning about floating-point arithmetic and directed rounding modes. We experimentally demonstrate that the verified implementation is competitive with state-of-the-art tools for MDPs, while providing formal guarantees on the correctness of the results.

cs.LO↗

arXiv2025

A Formal Analysis of Algorithms for Matroids and Greedoids

We present a formal analysis, in Isabelle/HOL, of optimisation algorithms for matroids, which are useful generalisations of combinatorial structures that occur in optimisation, and greedoids, which are a generalisation of matroids. Although some formalisation work has been done earlier on matroids, our work here presents the first formalisation of results on greedoids, and many results we formalise in relation to matroids are also formalised for the first time in this work. We formalise the analysis of a number of optimisation algorithms for matroids and greedoids. We also derive from those algorithms executable implementations of Kruskal's algorithm for minimum spanning trees, an algorithm for maximum cardinality matching for bi-partite graphs, and Prim's algorithm for computing minimum weight spanning trees.

cs.LO↗

arXiv2025

Proof-Producing Translation of Functional Programs into a Time \& Space Reasonable Model

We present a semi-automated framework to construct and reason about programs in a deeply-embedded while-language. The while-language we consider is a simple computation model that can simulate (and be simulated by) Turing Machines with a quadratic time and constant space blow-up. Our framework derives while-programs from functional programs written in a subset of Isabelle/HOL, namely tail-recursive functions with first-order arguments and algebraic datatypes. As far as we are aware, it is the first framework targeting a computation model that is reasonable in time and space from a complexity-theoretic perspective.

cs.LO↗

arXiv2024

Formally Verified Approximate Policy Iteration

We formally verify an algorithm for approximate policy iteration on Factored Markov Decision Processes using the interactive theorem prover Isabelle/HOL. Next, we show how the formalized algorithm can be refined to an executable, verified implementation. The implementation is evaluated on benchmark problems to show its practicability. As part of the refinement, we develop verified software to certify Linear Programming solutions. The algorithm builds on a diverse library of formalized mathematics and pushes existing methodologies for interactive theorem provers to the limits. We discuss the process of the verification project and the modifications to the algorithm needed for formal verification.

cs.AI↗

arXiv2023

Formally Verified Solution Methods for Infinite-Horizon Markov Decision Processes

We formally verify executable algorithms for solving Markov decision processes (MDPs) in the interactive theorem prover Isabelle/HOL. We build on existing formalizations of probability theory to analyze the expected total reward criterion on infinite-horizon problems. Our developments formalize the Bellman equation and give conditions under which optimal policies exist. Based on this analysis, we verify dynamic programming algorithms to solve tabular MDPs. We evaluate the formally verified implementations experimentally on standard problems and show they are practical. Furthermore, we show that, combined with efficient unverified implementations, our system can compete with and even outperform state-of-the-art systems.

cs.AI↗

arXiv2023

Formally Verified SAT-Based AI Planning

We present an executable formally verified SAT encoding of classical AI planning. We use the theorem prover Isabelle/HOL to perform the verification. We experimentally test the verified encoding and show that it can be used for reasonably sized standard planning benchmarks. We also use it as a reference to test a state-of-the-art SAT-based planner, showing that it sometimes falsely claims that problems have no solutions of certain lengths.

cs.AI↗

arXiv2023

A Formal Analysis of RANKING

We describe a formal correctness proof of RANKING, an online algorithm for online bipartite matching. An outcome of our formalisation is that it shows that there is a gap in all combinatorial proofs of the algorithm. Filling that gap constituted the majority of the effort which went into this work. This is despite the algorithm being one of the most studied algorithms and a central result in theoretical computer science. This gap is an example of difficulties in formalising graphical arguments which are ubiquitous in the theory of computing.

cs.LO↗

arXiv2022

Formal Semantics and Formally Verified Validation for Temporal Planning

We present a simple and concise semantics for temporal planning. Our semantics are developed and formalised in the logic of the interactive theorem prover Isabelle/HOL. We derive from those semantics a validation algorithm for temporal planning and show, using a formal proof in Isabelle/HOL, that this validation algorithm implements our semantics. We experimentally evaluate our verified validation algorithm and show that it is practical.

cs.AI↗

arXiv2021

Cost Optimal Planning as Satisfiability

We investigate upper bounds on the length of cost optimal plans that are valid for problems with 0-cost actions. We employ these upper bounds as horizons for a SAT-based encoding of planning with costs. Given an initial upper bound on the cost of the optimal plan, we experimentally show that this SAT-based approach is able to compute plans with better costs, and in many cases it can match the optimal cost. Also, in multiple instances, the approach is successful in proving that a certain cost is the optimal plan cost.

cs.AI↗

arXiv2021

Computing Plan-Length Bounds Using Lengths of Longest Paths

We devise a method to exactly compute the length of the longest simple path in factored state spaces, like state spaces encountered in classical planning. Although the complexity of this problem is NEXP-Hard, we show that our method can be used to compute practically useful upper-bounds on lengths of plans. We show that the computed upper-bounds are significantly (in many cases, orders of magnitude) better than bounds produced by previous bounding techniques and that they can be used to improve the SAT-based planning.

cs.AI↗

arXiv2019

Trustworthy Graph Algorithms

The goal of the LEDA project was to build an easy-to-use and extendable library of correct and efficient data structures, graph algorithms and geometric algorithms. We report on the use of formal program verification to achieve an even higher level of trustworthiness. Specifically, we report on an ongoing and largely finished verification of the blossom-shrinking algorithm for maximum cardinality matching.

cs.DS↗