Distributed Adaptive Optimization With Weight-Balancing
Dongdong Yue, Simone Baldi, Jinde Cao, Bart De Schutter
Abstract
This article addresses the continuous-time distributed optimization of a strictly convex summation-separable cost function with possibly nonconvex local functions over strongly connected digraphs. Distributed optimization methods in the literature require convexity of local functions, or balanced weights, or vanishing step sizes, or algebraic information (eigenvalues or eigenvectors) of the Laplacian matrix. The solution proposed here covers both weight-balanced and unbalanced digraphs in a unified way, without any of the aforementioned requirements.
Topics & Concepts
ConvexityEigenvalues and eigenvectorsLaplacian matrixMathematical optimizationMathematicsMinimum weightConvex functionSeparable spaceRegular polygonLaplace operatorAlgebraic numberConvex optimizationOptimization problemComputer scienceCombinatoricsMathematical analysisGeometryFinancial economicsPhysicsQuantum mechanicsEconomicsDistributed Control Multi-Agent SystemsNeural Networks Stability and SynchronizationAdvanced Memory and Neural Computing