Distributed Optimization with Guaranteed Constraint Satisfaction
The authors propose distributed optimization algorithms that can produce violation-free solutions to problems with separable convex objective functions and coupling constraints, while also converging to precise solutions with explicit rate guarantees.
Continuous-Time Distributed Optimization Algorithm for Safe Multi-Agent Control
A continuous-time distributed optimization algorithm is proposed that guarantees zero coupling constraint violation and asymptotically converges to the optimal solution of a centralized problem with coupled linear constraints.
Differentially-Private Constrained Consensus and Distributed Optimization with Guaranteed Convergence
The authors propose the first distributed constrained optimization algorithm that can ensure both provable convergence to a global optimal solution and rigorous ε-differential privacy, even when the number of iterations tends to infinity.
Efficient Distributed Algorithms for Solving Variational Inequalities and Saddle Point Problems
The paper presents a new distributed algorithm, called Three Pillars Algorithm, that combines three key techniques - similarity, compression, and local steps - to efficiently solve variational inequalities and saddle point problems. The algorithm achieves the best theoretical communication complexity compared to existing methods.
Distributed Optimization with Globally Coupled Equality Constraint: Implicit Tracking-Based Algorithm Design and Analysis
The core message of this work is to design efficient distributed algorithms, IDEA and Proj-IDEA, to solve a class of distributed optimization problems with a globally coupled equality constraint and local constrained sets. The key innovation is the novel implicit tracking approach, which allows the distributed algorithms to converge without the need of the strict convexity of local cost functions.
Scalable Distributed Optimization Despite Byzantine Adversaries: Algorithms for Multi-Dimensional Functions
The authors present resilient distributed optimization algorithms for multi-dimensional functions to mitigate the impact of Byzantine adversaries, ensuring convergence to a bounded region.
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