Print Email Facebook Twitter Distributed Optimisation with Linear Equality and Inequality Constraints using PDMM Title Distributed Optimisation with Linear Equality and Inequality Constraints using PDMM Author Heusdens, R. (TU Delft Signal Processing Systems; Netherlands Defense Academy (NLDA)) Zhang, Guoqiang (University of Exeter) Date 2024 Abstract In this article, we consider the problem of distributed optimisation of a separable convex cost function over a graph, where every edge and node in the graph could carry both linear equality and/or inequality constraints. We show how to modify the primal-dual method of multipliers (PDMM), originally designed for linear equality constraints, such that it can handle inequality constraints as well. The proposed algorithm does not need any slack variables, which is similar to the recent work (He et al., 2023) which extends the alternating direction method of multipliers (ADMM) for addressing decomposable optimisation with linear equality and inequality constraints. Using convex analysis, monotone operator theory and fixed-point theory, we show how to derive the update equations of the modified PDMM algorithm by applying Peaceman-Rachford splitting to the monotonic inclusion related to the lifted dual problem. To incorporate the inequality constraints, we impose a non-negativity constraint on the associated dual variables. This additional constraint results in the introduction of a reflection operator to model the data exchange in the network, instead of a permutation operator as derived for equality constraint PDMM. Convergence for both synchronous and stochastic update schemes of PDMM are provided. The latter includes asynchronous update schemes and update schemes with transmission losses. Experiments show that PDMM converges notably faster than extended ADMM of (He et al., 2023). Subject Convex optimizationdistributed optimizationlinear programmingPDMM To reference this document use: http://resolver.tudelft.nl/uuid:6a6fc8e6-f344-43ea-9127-81acb8246f8c DOI https://doi.org/10.1109/TSIPN.2024.3375597 Embargo date 2024-09-11 ISSN 2373-776X Source IEEE Transactions on Signal and Information Processing over Networks, 10, 294-306 Bibliographical note Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type journal article Rights © 2024 R. Heusdens, Guoqiang Zhang Files file embargo until 2024-09-11