Tootfinder

Swift is getting more delightful features for concurrency, love it: https://github.com/rjmccall/swift-evolution/blob/isolated-any-functions/proposals/NNNN-isolated-any-functions.md

swift-evolution/proposals/NNNN-isolated-any-functions.md at isolated-any-functions · rjmccall/swift-evolution
This maintains proposals for changes and user-visible enhancements to the Swift Programming Language. - rjmccall/swift-evolution

Inexact Policy Iteration Methods for Large-Scale Markov Decision Processes
Matilde Gargiani, Robin Sieber, Efe Balta, Dominic Liao-McPherson, John Lygeros
https://arxiv.org/abs/2404.06136

Inexact Policy Iteration Methods for Large-Scale Markov Decision Processes
We consider inexact policy iteration methods for large-scale infinite-horizon discounted MDPs with finite spaces, a variant of policy iteration where the policy evaluation step is implemented inexactly using an iterative solver for linear systems. In the classical dynamic programming literature, a similar principle is deployed in optimistic policy iteration, where an a-priori fixed-number of iterations of value iteration is used to inexactly solve the policy evaluation step. Inspired by the con…

Quantum Markov Decision Processes Part II: Optimal Solutions and Algorithms
Naci Saldi, Sina Sanjari, Serdar Yuksel
https://arxiv.org/abs/2402.14651 https:…

Quantum Markov Decision Processes Part II: Optimal Solutions and Algorithms
This two-part article aims to introduce a quantum analogue to classical Markov decision processes (MDPs). In Part II, building on the formulation of q-MDPs presented in Part I, our focus shifts to the development of algorithms for computing optimal policies and value functions of both open-loop and closed-loop policies. First, by using the duality between the dynamic programming and the semi-definite programming formulations of any q-MDP with open-loop policies, we establish an algorithm that e…

Quantum Markov Decision Processes Part I: General Theory, Approximations, and Classes of Policies
Naci Saldi, Sina Sanjari, Serdar Yuksel
https://arxiv.org/abs/2402.14649

Quantum Markov Decision Processes Part I: General Theory, Approximations, and Classes of Policies
In this two part article, the aim is to develop a quantum counterpart to classical Markov decision processes (MDPs). In Part I, we provide a very general formulation of quantum MDPs with state and action spaces in the quantum domain, quantum transitions, and cost functions. Once we formulate the quantum MDP (q-MDP), our focus shifts to establishing the verification theorem that proves the sufficiency of Markovian quantum control policies and provides a dynamic programming principle. Subsequentl…

An Empirical Evaluation of Pre-trained Large Language Models for Repairing Declarative Formal Specifications
Mohannad Alhanahnah, Md Rashedul Hasan, Hamid Bagheri
https://arxiv.org/abs/2404.11050

An Empirical Evaluation of Pre-trained Large Language Models for Repairing Declarative Formal Specifications
Automatic Program Repair (APR) has garnered significant attention as a practical research domain focused on automatically fixing bugs in programs. While existing APR techniques primarily target imperative programming languages like C and Java, there is a growing need for effective solutions applicable to declarative software specification languages. This paper presents a systematic investigation into the capacity of Large Language Models (LLMs) for repairing declarative specifications in Alloy,…