4 research outputs found
An Asynchronous, Decentralized Solution Framework for the Large Scale Unit Commitment Problem
With increased reliance on cyber infrastructure, large scale power networks
face new challenges owing to computational scalability. In this paper we focus
on developing an asynchronous decentralized solution framework for the Unit
Commitment(UC) problem for large scale power networks. We exploit the inherent
asynchrony in a region based decomposition arising out of imbalance in regional
subproblems to boost computational efficiency. A two phase algorithm is
proposed that relies on the convex relaxation and privacy preserving valid
inequalities in order to deliver algorithmic improvements. Our algorithm
employs a novel interleaved binary mechanism that locally switches from the
convex subproblem to its binary counterpart based on consistent local
convergent behavior. We develop a high performance computing (HPC) oriented
software framework that uses Message Passing Interface (MPI) to drive our
benchmark studies. Our simulations performed on the IEEE 3012 bus case are
benchmarked against the centralized and a state of the art synchronous
decentralized method. The results demonstrate that the asynchronous method
improves computational efficiency by a significant amount and provides a
competitive solution quality rivaling the benchmark methods
Fully Asynchronous Policy Evaluation in Distributed Reinforcement Learning over Networks
This paper proposes a \emph{fully asynchronous} scheme for the policy
evaluation problem of distributed reinforcement learning (DisRL) over directed
peer-to-peer networks. Without waiting for any other node of the network, each
node can locally update its value function at any time by using (possibly
delayed) information from its neighbors. This is in sharp contrast to the
gossip-based scheme where a pair of nodes concurrently update. Though the fully
asynchronous setting involves a difficult multi-timescale decision problem, we
design a novel stochastic average gradient (SAG) based distributed algorithm
and develop a push-pull augmented graph approach to prove its exact convergence
at a linear rate of where and increases by
one no matter on which node updates. Finally, numerical experiments validate
that our method speeds up linearly with respect to the number of nodes, and is
robust to straggler nodes
AsySPA: An Exact Asynchronous Algorithm for Convex Optimization Over Digraphs
This paper proposes a novel exact distributed asynchronous subgradient-push
algorithm (AsySPA) to solve an additive cost optimization problem over directed
graphs where each node only has access to a local convex function and updates
asynchronously with an arbitrary rate. Specifically, each node of a strongly
connected digraph does not wait for updates from other nodes but simply starts
a new update within any bounded time interval by using local information
available from its in-neighbors. "Exact" means that every node of the AsySPA
can asymptotically converge to the same optimal solution, even under different
update rates among nodes and bounded communication delays. To address uneven
update rates, we design a simple mechanism to adaptively adjust stepsizes per
update in each node, which is substantially different from the existing works.
Then, we construct a delay-free augmented system to address asynchrony and
delays, and study its convergence by proposing a generalized subgradient
algorithm, which clearly has its own significance and helps us to explicitly
evaluate the convergence rate of the AsySPA. Finally, we demonstrate advantages
of the AsySPA in both theory and simulation.Comment: Accepted by IEEE Transactions on Automatic Control. 15 pages, 9
figure
Asynchronous Gradient-Push
We consider a multi-agent framework for distributed optimization where each
agent has access to a local smooth strongly convex function, and the collective
goal is to achieve consensus on the parameters that minimize the sum of the
agents' local functions. We propose an algorithm wherein each agent operates
asynchronously and independently of the other agents. When the local functions
are strongly-convex with Lipschitz-continuous gradients, we show that the
iterates at each agent converge to a neighborhood of the global minimum, where
the neighborhood size depends on the degree of asynchrony in the multi-agent
network. When the agents work at the same rate, convergence to the global
minimizer is achieved. Numerical experiments demonstrate that Asynchronous
Gradient-Push can minimize the global objective faster than state-of-the-art
synchronous first-order methods, is more robust to failing or stalling agents,
and scales better with the network size.Comment: 33 pages, 9 figures, accepted to IEEE Transactions on Automatic
Contro