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- Vijay V. Desai, Vivek F. Farias, Ciamac C. Moallemi
- Management Science
- 2012

We introduce the pathwise optimization (PO) method, a new convex optimization procedure to produce upper and lower bounds on the optimal value (the 'price') of a high-dimensional optimal stopping problem. The PO method builds on a dual characterization of optimal stopping problems as optimization problems over the space of martingales, which we dub the… (More)

- Vijay V. Desai, Vivek F. Farias, Ciamac C. Moallemi
- NIPS
- 2009

We present a novel linear program for the approximation of the dynamic programming cost-to-go function in high-dimensional stochastic control problems. LP approaches to approximate DP naturally restrict attention to approximations that are lower bounds to the optimal cost-to-go function. Our program – the 'smoothed approximate linear program' – relaxes this… (More)

- Ciamac C. Moallemi, Benjamin Van Roy
- IEEE Transactions on Information Theory
- 2005

We propose consensus propagation, an asynchronous distributed protocol for averaging numbers across a network. We establish convergence, characterize the convergence rate for regular graphs, and demonstrate that the protocol exhibits better scaling properties than pairwise averaging, an alternative that has received much recent attention. Consensus… (More)

- Jason M. Johnson, Keith Mason, Ciamac C. Moallemi, Hualin Xi, Shyamal Somaroo, Enoch S. Huang
- Bioinformatics
- 2003

SUMMARY
The Pfaat protein family alignment annotation tool is a Java-based multiple sequence alignment editor and viewer designed for protein family analysis. The application merges display features such as dendrograms, secondary and tertiary protein structure with SRS retrieval, subgroup comparison, and extensive user-annotation capabilities.
… (More)

- Vijay V. Desai, Vivek F. Farias, Ciamac C. Moallemi
- Operations Research
- 2012

We present a novel linear program for the approximation of the dynamic programming cost-to-go function in high-dimensional stochastic control problems. LP approaches to approximate DP have typically relied on a natural 'projection' of a well studied linear program for exact dynamic programming. Such programs restrict attention to approximations that are… (More)

- Ciamac C. Moallemi, Benjamin Van Roy
- IEEE Transactions on Information Theory
- 2010

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… (More)

- Vivek F. Farias, Ciamac C. Moallemi, Benjamin Van Roy, Tsachy Weissman
- IEEE Transactions on Information Theory
- 2010

We consider an agent interacting with an unmodeled environment. At each time, the agent makes an observation, takes an action, and incurs a cost. Its actions can influence future observations and costs. The goal is to minimize the long-term average cost. We propose a novel algorithm, known as the active LZ algorithm, for optimal control based on ideas from… (More)

We consider the problem of producing lower bounds on the optimal cost-to-go function of a Markov decision problem. We present two approaches to this problem: one based on the methodology of approximate linear programming (ALP) and another based on the so-called martingale duality approach. We show that these two approaches are intimately connected.… (More)

- Chen Chen, Garud Iyengar, Ciamac C. Moallemi
- Management Science
- 2013

Systemic risk is an issue of great concern in modern financial markets as well as, more broadly, in the management of complex systems. We propose an axiomatic framework for systemic risk. Our framework allows for an independent specification of (1) a functional of the cross-sectional profile of outcomes across agents in the system in a single scenario of… (More)

- Ciamac C. Moallemi, Benjamin Van Roy
- IEEE Transactions on Information Theory
- 2009

We establish the convergence of the min-sum message passing algorithm for minimization of a quadratic objective function given a convex decomposition. Our results also apply to the equivalent problem of the convergence of Gaussian belief propagation.