2 citations · 3 across the 7 of their papers we have counts for
8 papers
When Algebraic Symmetry Breaking Meets Solvers: An Experimental Study
Madalina Erascu, Johannes Middeke
We present an experimental evaluation of automatically generated polynomial symmetry breaking constraints for integer linear programs. Starting from the method that we introduced a…
Automatic Generation of Polynomial Symmetry Breaking Constraints
Madalina Erascu, Johannes Middeke
Symmetry in integer programming causes redundant search and is often handled with symmetry breaking constraints that remove as many equivalent solutions as possible. We propose an…
Neuro-Symbolic Constrained Optimization for Cloud Application Deployment via Graph Neural Networks and Satisfiability Modulo Theory
Madalina Erascu
This paper proposes a novel hybrid neuro-symbolic framework for the optimal and scalable deployment of component-based applications in the Cloud. The challenge of efficiently mappi…
Benchmarking Local Robustness of High-Accuracy Binary Neural Networks for Enhanced Traffic Sign Recognition
Andreea Postovan, Mădălina Eraşcu
Traffic signs play a critical role in road safety and traffic management for autonomous driving systems. Accurate traffic sign classification is essential but challenging due to re…
SAGE -- A Tool for Optimal Deployments in Kubernetes Clusters
Vlad-Ioan Luca, Madalina Erascu
Cloud computing has brought a fundamental transformation in how organizations operate their applications, enabling them to achieve affordable high availability of services. Kuberne…
Benchmarking Optimization Solvers and Symmetry Breakers for the Automated Deployment of Component-based Applications in the Cloud (EXTENDED ABSTRACT)
Bogdan David, Madalina Erascu
Optimization solvers based on methods from constraint programming (OR-Tools, Chuffed, Gecode), optimization modulo theory (Z3), and mathematical programming (CPLEX) are successfull…
Architecturing Binarized Neural Networks for Traffic Sign Recognition
Andreea Postovan, Mădălina Eraşcu
Traffic signs support road safety and managing the flow of traffic, hence are an integral part of any vision system for autonomous driving. While the use of deep learning is well-k…
Scalable Optimal Deployment in the Cloud of Component-based Applications using Optimization Modulo Theory, Mathematical Programming and Symmetry Breaking
Madalina Erascu, Flavia Micota, Daniela Zaharie
The problem of Cloud resource provisioning for component-based applications consists in the allocation of virtual machines (VMs) offers from various Cloud Providers to a set of app…