Total algorithms

We define the notion of total algorithms for networks of processes. A total algorithm enforces that a "decision" is taken by a subset of the processes, and that participation of all processes is required to reach this decision. Total algorithms are an important building block in the design of distributed algorithms. For some important network control problems it can be shown that an algorithm solving it is necessarily total, and that any total algorithm can solve the problem. We study some total algorithms for a variety of network topologies. Constructions are shown to derive algorithms for Mutual Exclusion, Election, and Distributed Infirnum Approximation from arbitrary total algorithms. The paper puts many results and paradigms about designing distributed algorithms in a general framework. This report oulines several other works of the author. Total algorithms, their properties, and some additional examples, as well as traversal algorithms and the time complexity of distributed algorithms are studied in [Tel94, Chap.6]. The construction of algorithms for distributed infirnum approximation is treated in [CBT94, Tel86] and [Tel91, Sec. 4.1]

Iterative algorithms for total variation-like reconstructions in seismic tomography

A qualitative comparison of total variation like penalties (total variation, Huber variant of total variation, total generalized variation, ...) is made in the context of global seismic tomography. Both penalized and constrained formulations of seismic recovery problems are treated. A number of simple iterative recovery algorithms applicable to these problems are described. The convergence speed of these algorithms is compared numerically in this setting. For the constrained formulation a new algorithm is proposed and its convergence is proven.Comment: 28 pages, 8 figures. Corrected sign errors in formula (25

Multiclass Total Variation Clustering

Ideas from the image processing literature have recently motivated a new set of clustering algorithms that rely on the concept of total variation. While these algorithms perform well for bi-partitioning tasks, their recursive extensions yield unimpressive results for multiclass clustering tasks. This paper presents a general framework for multiclass total variation clustering that does not rely on recursion. The results greatly outperform previous total variation algorithms and compare well with state-of-the-art NMF approaches

Byzantine-Resistant Total Ordering Algorithms

AbstractMulticast group communication protocols are used extensively in fault-tolerant distributed systems. For many such protocols, the acknowledgments for individual messages define a causal order on messages. Maintaining the consistency of information, replicated on several processors to protect it against faults, is greatly simplified by a total order on messages. We present algorithms that incrementally convert a causal order on messages into a total order and that tolerate both crash and Byzantine process faults. Varying compromises between latency to message ordering and resilience to faults yield four distinct algorithms. All of these algorithms use a multistage voting strategy to achieve agreement on the total order and exploit the random structure of the causal order to ensure probabilistic termination