195 research outputs found
A Duality-Based Approach for Distributed Optimization with Coupling Constraints
In this paper we consider a distributed optimization scenario in which a set
of agents has to solve a convex optimization problem with separable cost
function, local constraint sets and a coupling inequality constraint. We
propose a novel distributed algorithm based on a relaxation of the primal
problem and an elegant exploration of duality theory. Despite its complex
derivation based on several duality steps, the distributed algorithm has a very
simple and intuitive structure. That is, each node solves a local version of
the original problem relaxation, and updates suitable dual variables. We prove
the algorithm correctness and show its effectiveness via numerical
computations
A randomized primal distributed algorithm for partitioned and big-data non-convex optimization
In this paper we consider a distributed optimization scenario in which the
aggregate objective function to minimize is partitioned, big-data and possibly
non-convex. Specifically, we focus on a set-up in which the dimension of the
decision variable depends on the network size as well as the number of local
functions, but each local function handled by a node depends only on a (small)
portion of the entire optimization variable. This problem set-up has been shown
to appear in many interesting network application scenarios. As main paper
contribution, we develop a simple, primal distributed algorithm to solve the
optimization problem, based on a randomized descent approach, which works under
asynchronous gossip communication. We prove that the proposed asynchronous
algorithm is a proper, ad-hoc version of a coordinate descent method and thus
converges to a stationary point. To show the effectiveness of the proposed
algorithm, we also present numerical simulations on a non-convex quadratic
program, which confirm the theoretical results
Minimum-time trajectory generation for quadrotors in constrained environments
In this paper, we present a novel strategy to compute minimum-time
trajectories for quadrotors in constrained environments. In particular, we
consider the motion in a given flying region with obstacles and take into
account the physical limitations of the vehicle. Instead of approaching the
optimization problem in its standard time-parameterized formulation, the
proposed strategy is based on an appealing re-formulation. Transverse
coordinates, expressing the distance from a frame path, are used to
parameterise the vehicle position and a spatial parameter is used as
independent variable. This re-formulation allows us to (i) obtain a fixed
horizon problem and (ii) easily formulate (fairly complex) position
constraints. The effectiveness of the proposed strategy is proven by numerical
computations on two different illustrative scenarios. Moreover, the optimal
trajectory generated in the second scenario is experimentally executed with a
real nano-quadrotor in order to show its feasibility.Comment: arXiv admin note: text overlap with arXiv:1702.0427
Controllability and observability of grid graphs via reduction and symmetries
In this paper we investigate the controllability and observability properties
of a family of linear dynamical systems, whose structure is induced by the
Laplacian of a grid graph. This analysis is motivated by several applications
in network control and estimation, quantum computation and discretization of
partial differential equations. Specifically, we characterize the structure of
the grid eigenvectors by means of suitable decompositions of the graph. For
each eigenvalue, based on its multiplicity and on suitable symmetries of the
corresponding eigenvectors, we provide necessary and sufficient conditions to
characterize all and only the nodes from which the induced dynamical system is
controllable (observable). We discuss the proposed criteria and show, through
suitable examples, how such criteria reduce the complexity of the
controllability (respectively observability) analysis of the grid
On the reachability and observability of path and cycle graphs
In this paper we investigate the reachability and observability properties of
a network system, running a Laplacian based average consensus algorithm, when
the communication graph is a path or a cycle. More in detail, we provide
necessary and sufficient conditions, based on simple algebraic rules from
number theory, to characterize all and only the nodes from which the network
system is reachable (respectively observable). Interesting immediate
corollaries of our results are: (i) a path graph is reachable (observable) from
any single node if and only if the number of nodes of the graph is a power of
two, , and (ii) a cycle is reachable (observable) from
any pair of nodes if and only if is a prime number. For any set of control
(observation) nodes, we provide a closed form expression for the (unreachable)
unobservable eigenvalues and for the eigenvectors of the (unreachable)
unobservable subsystem
A duality-based approach for distributed min-max optimization with application to demand side management
In this paper we consider a distributed optimization scenario in which a set
of processors aims at minimizing the maximum of a collection of "separable
convex functions" subject to local constraints. This set-up is motivated by
peak-demand minimization problems in smart grids. Here, the goal is to minimize
the peak value over a finite horizon with: (i) the demand at each time instant
being the sum of contributions from different devices, and (ii) the local
states at different time instants being coupled through local dynamics. The
min-max structure and the double coupling (through the devices and over the
time horizon) makes this problem challenging in a distributed set-up (e.g.,
well-known distributed dual decomposition approaches cannot be applied). We
propose a distributed algorithm based on the combination of duality methods and
properties from min-max optimization. Specifically, we derive a series of
equivalent problems by introducing ad-hoc slack variables and by going back and
forth from primal and dual formulations. On the resulting problem we apply a
dual subgradient method, which turns out to be a distributed algorithm. We
prove the correctness of the proposed algorithm and show its effectiveness via
numerical computations.Comment: arXiv admin note: substantial text overlap with arXiv:1611.0916
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