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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)

- 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)

- 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)

- 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)

We develop an approach based on temporal difference learning to address scheduling problems in complex queueing networks such as those arising in service, communication , and manufacturing systems. One novel feature is the selection of basis functions, which is motivated by the gross behavior of the system in asymptotic regimes. Another is the use of… (More)

We consider a broad class of dynamic portfolio optimization problems that allow for complex models of return predictability, transaction costs, trading constraints, and risk considerations. Determining an optimal policy in this general setting is almost always intractable. We propose a class of linear rebalancing rules, and describe an efficient… (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)