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Parallel and Distributed Computation: Numerical Methods
TLDR
This work discusses parallel and distributed architectures, complexity measures, and communication and synchronization issues, and it presents both Jacobi and Gauss-Seidel iterations, which serve as algorithms of reference for many of the computational approaches addressed later.
Introduction to linear optimization
p. 27, l. −11, replace “Schwartz” by “Schwarz” p. 69, l. −13: “ai∗x = bi” should be “ai∗x = bi∗” p. 126, l. 16, replace “inequality constraints” by “linear inequality constraints” p. 153, l. −8,…
Parallel and distributed computation
This book focuses on numerical algorithms suited for parallelization for solving systems of equations and optimization problems. Emphasis on relaxation methods of the Jacobi and Gauss-Seidel type,…
Actor-Critic Algorithms
TLDR
This thesis proposes and studies actor-critic algorithms which combine the above two approaches with simulation to find the best policy among a parameterized class of policies, and proves convergence of the algorithms for problems with general state and decision spaces.
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The Complexity of Markov Decision Processes
TLDR
All three variants of the classical problem of optimal policy computation in Markov decision processes, finite horizon, infinite horizon discounted, and infinite horizon average cost are shown to be complete for P, and therefore most likely cannot be solved by highly parallel algorithms.
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Analysis of Temporal-Diffference Learning with Function Approximation
We present new results about the temporal-difference learning algorithm, as applied to approximating the cost-to-go function of a Markov chain using linear function approximators. The algorithm we…
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An Analysis of Stochastic Shortest Path Problems
We consider a stochastic version of the classical shortest path problem whereby for each node of a graph, we must choose a probability distribution over the set of successor nodes so as to reach a…
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Convergence in Multiagent Coordination, Consensus, and Flocking
We discuss an old distributed algorithm for reaching consensus that has received a fair amount of recent attention. In this algorithm, a number of agents exchange their values asynchronously and form…
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Efficient algorithms for globally optimal trajectories
  • J. Tsitsiklis
  • Computer Science
    Proceedings of 33rd IEEE Conference on Decision…
  • 14 December 1994
Presents serial and parallel algorithms for solving a system of equations that arises from the discretization of the Hamilton-Jacobi equation associated to a trajectory optimization problem of the…
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Distributed Asynchronous Deterministic and Stochastic Gradient Optimization Algorithms
TLDR
A model for asynchronous distributed computation is presented and it is shown that natural asynchronous distributed versions of a large class of deterministic and stochastic gradient-like algorithms retain the desirable convergence properties of their centralized counterparts.
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