Distributed optimization methods allow us to decompose an optimization problem
into smaller, more manageable subproblems that are solved in parallel. For this
reason, they are widely used to solve large-scale problems arising in areas as diverse
as wireless communications, optimal control, machine learning, artificial intelligence,
computational biology, finance and statistics, to name a few. Moreover, distributed
algorithms avoid the cost and fragility associated with centralized coordination, and
provide better privacy for the autonomous decision makers. These are desirable
properties, especially in applications involving networked robotics, communication
or sensor networks, and power distribution systems.
In this thesis we propose the Accelerated Distributed Augmented Lagrangians
(ADAL) algorithm, a novel decomposition method for convex optimization prob-
lems with certain separability structure. The method is based on the augmented
Lagrangian framework and addresses problems that involve multiple agents optimiz-
ing a separable convex objective function subject to convex local constraints and
linear coupling constraints. We establish the convergence of ADAL and also show
that it has a worst-case O(1/k) convergence rate, where k denotes the number of
iterations.
Moreover, we show that ADAL converges to a local minimum of the problem
for cases with non-convex objective functions. This is the first published work that
formally establishes the convergence of a distributed augmented Lagrangian method
ivfor non-convex optimization problems. An alternative way to select the stepsizes
used in the algorithm is also discussed. These two contributions are independent
from each other, meaning that convergence of the non-convex ADAL method can
still be shown using the stepsizes from the convex case, and, similarly, convergence
of the convex ADAL method can be shown using the stepsizes proposed in the non-
convex proof.
Furthermore, we consider cases where the distributed algorithm needs to operate
in the presence of uncertainty and noise and show that the generated sequences of
primal and dual variables converge to their respective optimal sets almost surely. In
particular, we are concerned with scenarios where: i) the local computation steps
are inexact or are performed in the presence of uncertainty, and ii) the message
exchanges between agents are corrupted by noise. In this case, the proposed scheme
can be classified as a distributed stochastic approximation method. Compared to
existing literature in this area, our work is the first that utilizes the augmented
Lagrangian framework. Moreover, the method allows us to solve a richer class of
problems as compared to existing methods on distributed stochastic approximation
that consider only consensus constraints.
Extensive numerical experiments have been carried out in an effort to validate
the novelty and effectiveness of the proposed method in all the areas of the afore-
mentioned theoretical contributions. We examine problems in convex, non-convex,
and stochastic settings where uncertainties and noise affect the execution of the al-
gorithm. For the convex cases, we present applications of ADAL to certain popular
network optimization problems, as well as to a two-stage stochastic optimization
problem. The simulation results suggest that the proposed method outperforms
the state-of-the-art distributed augmented Lagrangian methods that are known in
the literature. For the non-convex cases, we perform simulations on certain simple
non-convex problems to establish that ADAL indeed converges to non-trivial local
vsolutions of the problems; in comparison, the straightforward implementation of the
other distributed augmented Lagrangian methods on the same problems does not
lead to convergence. For the stochastic setting, we present simulation results of
ADAL applied on network optimization problems and examine the effect that noise
and uncertainties have in the convergence behavior of the method.
As an extended and more involved application, we also consider the problem
of relay cooperative beamforming in wireless communications systems. Specifically,
we study the scenario of a multi-cluster network, in which each cluster contains
multiple single-antenna source destination pairs that communicate simultaneously
over the same channel. The communications are supported by cooperating amplify-
and-forward relays, which perform beamforming. Since the emerging problem is non-
convex, we propose an approximate convex reformulation. Based on ADAL, we also
discuss two different ways to obtain a distributed solution that allows for autonomous
computation of the optimal beamforming decisions by each cluster, while taking into
account intra- and inter-cluster interference effects.
Our goal in this thesis is to advance the state-of-the-art in distributed optimization by proposing methods that combine fast convergence, wide applicability, ease
of implementation, low computational complexity, and are robust with respect to
delays, uncertainty in the problem parameters, noise corruption in the message ex-
changes, and inexact computations.
","abstract_html":"<p>Distributed optimization methods allow us to decompose an optimization problem</p><p>into smaller, more manageable subproblems that are solved in parallel. For this</p><p>reason, they are widely used to solve large-scale problems arising in areas as diverse</p><p>as wireless communications, optimal control, machine learning, artificial intelligence,</p><p>computational biology, finance and statistics, to name a few. Moreover, distributed</p><p>algorithms avoid the cost and fragility associated with centralized coordination, and</p><p>provide better privacy for the autonomous decision makers. These are desirable</p><p>properties, especially in applications involving networked robotics, communication</p><p>or sensor networks, and power distribution systems.</p><p>In this thesis we propose the Accelerated Distributed Augmented Lagrangians</p><p>(ADAL) algorithm, a novel decomposition method for convex optimization prob-</p><p>lems with certain separability structure. The method is based on the augmented</p><p>Lagrangian framework and addresses problems that involve multiple agents optimiz-</p><p>ing a separable convex objective function subject to convex local constraints and</p><p>linear coupling constraints. We establish the convergence of ADAL and also show</p><p>that it has a worst-case O(1/k) convergence rate, where k denotes the number of</p><p>iterations.</p><p>Moreover, we show that ADAL converges to a local minimum of the problem</p><p>for cases with non-convex objective functions. This is the first published work that</p><p>formally establishes the convergence of a distributed augmented Lagrangian method</p><p>ivfor non-convex optimization problems. An alternative way to select the stepsizes</p><p>used in the algorithm is also discussed. These two contributions are independent</p><p>from each other, meaning that convergence of the non-convex ADAL method can</p><p>still be shown using the stepsizes from the convex case, and, similarly, convergence</p><p>of the convex ADAL method can be shown using the stepsizes proposed in the non-</p><p>convex proof.</p><p>Furthermore, we consider cases where the distributed algorithm needs to operate</p><p>in the presence of uncertainty and noise and show that the generated sequences of</p><p>primal and dual variables converge to their respective optimal sets almost surely. In</p><p>particular, we are concerned with scenarios where: i) the local computation steps</p><p>are inexact or are performed in the presence of uncertainty, and ii) the message</p><p>exchanges between agents are corrupted by noise. In this case, the proposed scheme</p><p>can be classified as a distributed stochastic approximation method. Compared to</p><p>existing literature in this area, our work is the first that utilizes the augmented</p><p>Lagrangian framework. Moreover, the method allows us to solve a richer class of</p><p>problems as compared to existing methods on distributed stochastic approximation</p><p>that consider only consensus constraints.</p><p>Extensive numerical experiments have been carried out in an effort to validate</p><p>the novelty and effectiveness of the proposed method in all the areas of the afore-</p><p>mentioned theoretical contributions. We examine problems in convex, non-convex,</p><p>and stochastic settings where uncertainties and noise affect the execution of the al-</p><p>gorithm. For the convex cases, we present applications of ADAL to certain popular</p><p>network optimization problems, as well as to a two-stage stochastic optimization</p><p>problem. The simulation results suggest that the proposed method outperforms</p><p>the state-of-the-art distributed augmented Lagrangian methods that are known in</p><p>the literature. For the non-convex cases, we perform simulations on certain simple</p><p>non-convex problems to establish that ADAL indeed converges to non-trivial local</p><p>vsolutions of the problems; in comparison, the straightforward implementation of the</p><p>other distributed augmented Lagrangian methods on the same problems does not</p><p>lead to convergence. For the stochastic setting, we present simulation results of</p><p>ADAL applied on network optimization problems and examine the effect that noise</p><p>and uncertainties have in the convergence behavior of the method.</p><p>As an extended and more involved application, we also consider the problem</p><p>of relay cooperative beamforming in wireless communications systems. Specifically,</p><p>we study the scenario of a multi-cluster network, in which each cluster contains</p><p>multiple single-antenna source destination pairs that communicate simultaneously</p><p>over the same channel. The communications are supported by cooperating amplify-</p><p>and-forward relays, which perform beamforming. Since the emerging problem is non-</p><p>convex, we propose an approximate convex reformulation. Based on ADAL, we also</p><p>discuss two different ways to obtain a distributed solution that allows for autonomous</p><p>computation of the optimal beamforming decisions by each cluster, while taking into</p><p>account intra- and inter-cluster interference effects.</p><p>Our goal in this thesis is to advance the state-of-the-art in distributed optimization by proposing methods that combine fast convergence, wide applicability, ease</p><p>of implementation, low computational complexity, and are robust with respect to</p><p>delays, uncertainty in the problem parameters, noise corruption in the message ex-</p><p>changes, and inexact computations.</p>","abstract_has_math":false,"creators":["Chatzipanagiotis, Nikolaos"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Zavlanos, Michael M."],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015","date_published":"2015","updated_at":"2026-07-24T02:07:01Z","subjects":["Mechanical engineering","Operations research","Mathematics","Distributed optimization","Networked control systems","Optimization algorithms","Wireless communications"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10161/11304","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Zavlanos, Michael M."]},{"key":"dc:creator","label":"Author","values":["Chatzipanagiotis, Nikolaos"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2016-01-04T19:25:24Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2016-01-04T19:25:24Z"]},{"key":"dc:date.issued","label":"Date","values":["2015"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Mechanical engineering","Operations research","Mathematics","Distributed optimization","Networked control systems","Optimization algorithms","Wireless communications"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10161/11304"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Distributed optimization methods allow us to decompose an optimization problem
into smaller, more manageable subproblems that are solved in parallel. For this
reason, they are widely used to solve large-scale problems arising in areas as diverse
as wireless communications, optimal control, machine learning, artificial intelligence,
computational biology, finance and statistics, to name a few. Moreover, distributed
algorithms avoid the cost and fragility associated with centralized coordination, and
provide better privacy for the autonomous decision makers. These are desirable
properties, especially in applications involving networked robotics, communication
or sensor networks, and power distribution systems.
In this thesis we propose the Accelerated Distributed Augmented Lagrangians
(ADAL) algorithm, a novel decomposition method for convex optimization prob-
lems with certain separability structure. The method is based on the augmented
Lagrangian framework and addresses problems that involve multiple agents optimiz-
ing a separable convex objective function subject to convex local constraints and
linear coupling constraints. We establish the convergence of ADAL and also show
that it has a worst-case O(1/k) convergence rate, where k denotes the number of
iterations.
Moreover, we show that ADAL converges to a local minimum of the problem
for cases with non-convex objective functions. This is the first published work that
formally establishes the convergence of a distributed augmented Lagrangian method
ivfor non-convex optimization problems. An alternative way to select the stepsizes
used in the algorithm is also discussed. These two contributions are independent
from each other, meaning that convergence of the non-convex ADAL method can
still be shown using the stepsizes from the convex case, and, similarly, convergence
of the convex ADAL method can be shown using the stepsizes proposed in the non-
convex proof.
Furthermore, we consider cases where the distributed algorithm needs to operate
in the presence of uncertainty and noise and show that the generated sequences of
primal and dual variables converge to their respective optimal sets almost surely. In
particular, we are concerned with scenarios where: i) the local computation steps
are inexact or are performed in the presence of uncertainty, and ii) the message
exchanges between agents are corrupted by noise. In this case, the proposed scheme
can be classified as a distributed stochastic approximation method. Compared to
existing literature in this area, our work is the first that utilizes the augmented
Lagrangian framework. Moreover, the method allows us to solve a richer class of
problems as compared to existing methods on distributed stochastic approximation
that consider only consensus constraints.
Extensive numerical experiments have been carried out in an effort to validate
the novelty and effectiveness of the proposed method in all the areas of the afore-
mentioned theoretical contributions. We examine problems in convex, non-convex,
and stochastic settings where uncertainties and noise affect the execution of the al-
gorithm. For the convex cases, we present applications of ADAL to certain popular
network optimization problems, as well as to a two-stage stochastic optimization
problem. The simulation results suggest that the proposed method outperforms
the state-of-the-art distributed augmented Lagrangian methods that are known in
the literature. For the non-convex cases, we perform simulations on certain simple
non-convex problems to establish that ADAL indeed converges to non-trivial local
vsolutions of the problems; in comparison, the straightforward implementation of the
other distributed augmented Lagrangian methods on the same problems does not
lead to convergence. For the stochastic setting, we present simulation results of
ADAL applied on network optimization problems and examine the effect that noise
and uncertainties have in the convergence behavior of the method.
As an extended and more involved application, we also consider the problem
of relay cooperative beamforming in wireless communications systems. Specifically,
we study the scenario of a multi-cluster network, in which each cluster contains
multiple single-antenna source destination pairs that communicate simultaneously
over the same channel. The communications are supported by cooperating amplify-
and-forward relays, which perform beamforming. Since the emerging problem is non-
convex, we propose an approximate convex reformulation. Based on ADAL, we also
discuss two different ways to obtain a distributed solution that allows for autonomous
computation of the optimal beamforming decisions by each cluster, while taking into
account intra- and inter-cluster interference effects.
Our goal in this thesis is to advance the state-of-the-art in distributed optimization by proposing methods that combine fast convergence, wide applicability, ease
of implementation, low computational complexity, and are robust with respect to
delays, uncertainty in the problem parameters, noise corruption in the message ex-
changes, and inexact computations.
"]},{"key":"dc:title","label":"Title","values":["Distributed Optimization Algorithms for Networked Systems"]}]}],"canonical_facts":{"dc:contributor.advisor":["Zavlanos, Michael M."],"dc:creator":["Chatzipanagiotis, Nikolaos"],"dc:date.accessioned":["2016-01-04T19:25:24Z"],"dc:date.available":["2016-01-04T19:25:24Z"],"dc:date.issued":["2015"],"dc:description.abstract":["Distributed optimization methods allow us to decompose an optimization problem
into smaller, more manageable subproblems that are solved in parallel. For this
reason, they are widely used to solve large-scale problems arising in areas as diverse
as wireless communications, optimal control, machine learning, artificial intelligence,
computational biology, finance and statistics, to name a few. Moreover, distributed
algorithms avoid the cost and fragility associated with centralized coordination, and
provide better privacy for the autonomous decision makers. These are desirable
properties, especially in applications involving networked robotics, communication
or sensor networks, and power distribution systems.
In this thesis we propose the Accelerated Distributed Augmented Lagrangians
(ADAL) algorithm, a novel decomposition method for convex optimization prob-
lems with certain separability structure. The method is based on the augmented
Lagrangian framework and addresses problems that involve multiple agents optimiz-
ing a separable convex objective function subject to convex local constraints and
linear coupling constraints. We establish the convergence of ADAL and also show
that it has a worst-case O(1/k) convergence rate, where k denotes the number of
iterations.
Moreover, we show that ADAL converges to a local minimum of the problem
for cases with non-convex objective functions. This is the first published work that
formally establishes the convergence of a distributed augmented Lagrangian method
ivfor non-convex optimization problems. An alternative way to select the stepsizes
used in the algorithm is also discussed. These two contributions are independent
from each other, meaning that convergence of the non-convex ADAL method can
still be shown using the stepsizes from the convex case, and, similarly, convergence
of the convex ADAL method can be shown using the stepsizes proposed in the non-
convex proof.
Furthermore, we consider cases where the distributed algorithm needs to operate
in the presence of uncertainty and noise and show that the generated sequences of
primal and dual variables converge to their respective optimal sets almost surely. In
particular, we are concerned with scenarios where: i) the local computation steps
are inexact or are performed in the presence of uncertainty, and ii) the message
exchanges between agents are corrupted by noise. In this case, the proposed scheme
can be classified as a distributed stochastic approximation method. Compared to
existing literature in this area, our work is the first that utilizes the augmented
Lagrangian framework. Moreover, the method allows us to solve a richer class of
problems as compared to existing methods on distributed stochastic approximation
that consider only consensus constraints.
Extensive numerical experiments have been carried out in an effort to validate
the novelty and effectiveness of the proposed method in all the areas of the afore-
mentioned theoretical contributions. We examine problems in convex, non-convex,
and stochastic settings where uncertainties and noise affect the execution of the al-
gorithm. For the convex cases, we present applications of ADAL to certain popular
network optimization problems, as well as to a two-stage stochastic optimization
problem. The simulation results suggest that the proposed method outperforms
the state-of-the-art distributed augmented Lagrangian methods that are known in
the literature. For the non-convex cases, we perform simulations on certain simple
non-convex problems to establish that ADAL indeed converges to non-trivial local
vsolutions of the problems; in comparison, the straightforward implementation of the
other distributed augmented Lagrangian methods on the same problems does not
lead to convergence. For the stochastic setting, we present simulation results of
ADAL applied on network optimization problems and examine the effect that noise
and uncertainties have in the convergence behavior of the method.
As an extended and more involved application, we also consider the problem
of relay cooperative beamforming in wireless communications systems. Specifically,
we study the scenario of a multi-cluster network, in which each cluster contains
multiple single-antenna source destination pairs that communicate simultaneously
over the same channel. The communications are supported by cooperating amplify-
and-forward relays, which perform beamforming. Since the emerging problem is non-
convex, we propose an approximate convex reformulation. Based on ADAL, we also
discuss two different ways to obtain a distributed solution that allows for autonomous
computation of the optimal beamforming decisions by each cluster, while taking into
account intra- and inter-cluster interference effects.
Our goal in this thesis is to advance the state-of-the-art in distributed optimization by proposing methods that combine fast convergence, wide applicability, ease
of implementation, low computational complexity, and are robust with respect to
delays, uncertainty in the problem parameters, noise corruption in the message ex-
changes, and inexact computations.
"],"dc:identifier.uri":["https://hdl.handle.net/10161/11304"],"dc:subject":["Mechanical engineering","Operations research","Mathematics","Distributed optimization","Networked control systems","Optimization algorithms","Wireless communications"],"dc:title":["Distributed Optimization Algorithms for Networked Systems"],"dc:type":["Dissertation"]},"updated_at":"2026-07-24T02:07:01Z"}