Matrix analysis
Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
Convex Optimization
Survey propagation: An algorithm for satisfiability
Random Structures & Algorithms
Walk-Sums and Belief Propagation in Gaussian Graphical Models
The Journal of Machine Learning Research
Convergence of min-sum message passing for quadratic optimization
IEEE Transactions on Information Theory
Message passing for maximum weight independent set
IEEE Transactions on Information Theory
The capacity of low-density parity-check codes under message-passing decoding
IEEE Transactions on Information Theory
An analysis of belief propagation on the turbo decoding graph with Gaussian densities
IEEE Transactions on Information Theory
Tree-based reparameterization framework for analysis of sum-product and related algorithms
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Max-Product for Maximum Weight Matching: Convergence, Correctness, and LP Duality
IEEE Transactions on Information Theory
Linear coordinate-descent message passing for quadratic optimization
Neural Computation
Message-passing algorithms for quadratic minimization
The Journal of Machine Learning Research
| Hi-index | 754.84 |
We establish that the min-sum message-passing algorithm and its asynchronous variants converge for a large class of unconstrained convex optimization problems, generalizing existing results for pairwise quadratic optimization problems. The main sufficient condition is that of scaled diagonal dominance. This condition is similar to known sufficient conditions for asynchronous convergence of other decentralized optimization algorithms, such as coordinate descent and gradient descent.