coin-or/Cbc: Version 2.10.5

johnjforrest,; Vigerske, Stefan; Santos, Haroldo Gambini; Ralphs, Ted K.; Hafer, Lou; Kristjánsson, Bjarni Reyr; jpfasano,; EdwinStraver,; Lubin, Miles; rlougee,; jpgoncal,; h-i-gassmann,; Saltzman, Matthew R.

doi:10.5281/zenodo.3700700

Cited by 13 publications

(1 citation statement)

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“…1. We use CVXPY [8] as our modeling language for convex optimization problems and CBC [18] as the LP solver. It is worth mentioning that we ignore the floating point error in our re-implementation of DeepPoly because sound linear programming currently does not scale in our experiments.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Improving Neural Network Verification through Spurious Region Guided Refinement

Yang¹,

Li²,

Li³

et al. 2020

Preprint

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We propose a spurious region guided refinement approach for robustness verification of deep neural networks. Our method starts with applying the DeepPoly abstract domain to analyze the network. If the robustness property cannot be verified, the result is inconclusive. Due to the over-approximation, the computed region in the abstraction may be spurious in the sense that it does not contain any true counterexample. Our goal is to identify such spurious regions and use them to guide the abstraction refinement. The core idea is to make use of the obtained constraints of the abstraction to infer new bounds for the neurons. This is achieved by linear programming techniques. With the new bounds, we iteratively apply DeepPoly, aiming to eliminate spurious regions. We have implemented our approach in a prototypical tool DeepSRGR. Experimental results show that a large amount of regions can be identified as spurious, and as a result, the precision of DeepPoly can be significantly improved. As a side contribution, we show that our approach can be applied to verify quantitative robustness properties.

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Section: Experimental Evaluationmentioning

confidence: 99%

Improving Neural Network Verification through Spurious Region Guided Refinement

Yang¹,

Li²,

Li³

et al. 2020

Preprint

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Optimising Antenna Positioning for Maximum Coverage: The Case Study of Cattle Tracking in Austrian Alps Using Long Range (LoRa) Based Monitoring System

Welscher,

Bulbul,

Scholz

et al. 2022

Lecture Notes in Computer Science

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A Quantum Annealing Approach for Solving Hard Variants of the Stable Marriage Problem

Roch

Winderl

Linnhoff‐Popien

et al. 2022

Innovations for Community Services

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The Stable Marriage Problem (SMP) describes the problem, of finding a stable matching between two equally sized sets of elements (e.g., males and females) given an ordering of preferences for each element. A matching is stable, when there does not exist any match of a male and female which both prefer each other to their current partner under the matching. Finding such a matching of maximum cardinality, when ties and incomplete preference lists are allowed, is called MAX-SMTI and is an NP-hard variation of the SMP.In this work a Quadratic Unconstrained Binary Optimization (QUBO) formulation for MAX-SMTI is introduced and solved both with D-Wave Systems quantum annealing hardware and by their classical meta-heuristic QBSolv. Both approaches are reviewed against existing state-of-the-art approximation algorithms for MAX-SMTI. Additionally, the proposed QUBO problem can also be used to count stable matchings in SMP instances, which is proven to be a #P-complete problem. The results show, that the proposed (quantum) methods can compete with the classical ones regarding the solution quality and might be a relevant alternative, when quantum hardware scales with respect to the number of qubits and their connectivity.

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coin-or/Cbc: Version 2.10.5

Cited by 13 publications

References 0 publications

Improving Neural Network Verification through Spurious Region Guided Refinement

Improving Neural Network Verification through Spurious Region Guided Refinement

Optimising Antenna Positioning for Maximum Coverage: The Case Study of Cattle Tracking in Austrian Alps Using Long Range (LoRa) Based Monitoring System

A Quantum Annealing Approach for Solving Hard Variants of the Stable Marriage Problem

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