<ul data-eligibleForWebStory="true"><li>Semidefinite programming (SDP) relaxation is a promising method for neural network verification, but faces a critical limitation known as interior-point vanishing when applied to deep networks.</li><li>Interior-point vanishing causes the loss of strict feasibility in SDP relaxation, impacting numerical stability and optimality.</li><li>Increasing the depth of deep neural networks can lead to the loss of strict feasibility, creating a barrier to scaling SDP-based verification.</li><li>Five solutions have been designed to improve the feasibility conditions of SDP-based verification, successfully solving a significant percentage of previously unsolved problems.</li><li>Analysis reveals that constraints inherited from prior work for ReLU units are not only unbeneficial but can also be harmful to problem feasibility.</li><li>This research sheds light on the challenges of SDP-based neural network verification and proposes practical solutions to enhance its applicability to deeper networks, ultimately contributing to the development of more reliable and secure systems.</li></ul>

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification

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