Following their conception in the mid twentieth century, the world of computers has evolved from a landscape of isolated entities into a sprawling web of interconnected machines. Yet, given this evolution, many of the methods we use for allowing computers to work together still reflect their inherently isolated origins with the aggregation of data or master-slave relationships still commonly seeing use. While sufficient for some types of applications, these approaches do not naturally reflect the collaboration strategies we observe in nature and so the question is raised as to whether we can do better? In parallel to the improvements in computer to computer communication, the emergence of new paradigms such as the Internet of Things (IoT), Big Data processing and cloud computing in recent years has placed an increasing importance on networked systems in many facets of the modern world. From power grid management, to autonomous vehicle navigation, to even our basic means of interaction through social media, these networks are a pervasive presence in our day to day lives. The vast amounts of data generated by these networks and their ever increasing sizes makes it impractical if not impossible to resort to traditional centralized processing and therefore necessitates the search for new methods of signal processing within networked systems. In this thesis we approach the task of distributed signal processing by exploiting the synergy between such tasks and equivalent convex optimization problems. Specifically, we focus on the task of distributed convex optimization, that of solving optimization problems involving groups of computers in a collaborative manner and the development of distributed solvers for such tasks. Such solvers distinguish themselves by only allowing local computations at each computer in a network and the exchange of information between connected computers. In this way, distributed solvers naturally respect the structure of the underlying network in which they are deployed. In the pursuit of our goal, we approach the task of distributed solver design via the lens of monotone operator theory. Providing a well known platform for the derivation of many first order convex solvers, herein we demonstrate the use of this theory as a means of constructing and analyzing a number of algorithms for distributed optimization. The first major contribution of this thesis lies in the analysis and understanding of an existing algorithm for distributed optimization within the literature termed the primal dual method of multipliers (PDMM). In particular, by demonstrating a novel interpretation of PDMM from the perspective of monotone operator theory we are able to better understand its convergent characteristics and highlight sufficient conditions for which PDMM will converge at a geometric rate. Furthermore we quantify the impact that network topology has on these convergence rates, drawing a direct connection between spectral characteristics of networks and distributed optimization. Secondly, we explored the space of solver design by proposing novel algorithms for distributed networks. For the family of separable optimization problems, those with separable objectives and constraints, we demonstrated a distributed solver design using a specific lifted dual form. Based on monotone operator theory, the convergence analysis of the proposed method followed naturally from well known results and broadened the class of distributable problems compared to the likes of PDMM. Furthermore, in the case of time-varying consensus problems, we again proposed a new algorithm by combining a network dependent metric choice with classic operator splitting methods. Again the monotone basis of this algorithm facilitated the convergence analysis of this method which empirically was also shown to converge for general closed, convex and proper functions. Finally, we demonstrated how these methods could be used for practical distributed signal processing in networks by considering the case of multichannel speech enhancement in wireless acoustic sensor networks. By combining a particular modeling of the acoustic scene with the algorithms mentioned above, the proposed method was not only distributable but also offered increased resilience to steering vector mismatch than other standard approaches. This example also highlights the importance of understanding both the target application and the distributed solvers themselves in developing effective solutions. Overall, this thesis provides a first foray into the world of distributed optimization via the lens of monotone operator theory. We feel that this perspective provides an ideal reference for the analysis of such algorithms while also providing a general framework for convex optimization solver design in turn. While this thesis is not the end of this branch of research, it indicates the potential of the monotone operator theory as a unifying method for the development and analysis of distributed optimization solutions.@en

Large-scale networks of computing units, often characterised by the absence of central control, have become commonplace in many applications. To facilitate data processing in these large-scale networks, distributed signal processing is required. The iterative behaviour of distributed processing algorithms combined with energy, computational power, and bandwidth limitations imposed by such networks, place tight constraints on the transmission capacities of the individual nodes. In this paper we investigate the effects of subtractive dithered uniform quantisation in PDMM for the synchronous distributed averaging problem. This is done by deriving expressions for the mean squared error (MSE) that include quantisation noise. Also, the required data rate for quantised PDMM is considered. It was found that for practical applications quantisation in PDMM can be applied with a fixed-rate quantiser, such that significant data rate reduction can be achieved, without compromising the rate of convergence.@en

Large-scale networks of computing units, often characterised by the absence of central control, have become commonplace in many applications. To facilitate data processing in these large-scale networks, distributed signal processing is required. The iterative behaviour of distributed processing algorithms combined with energy, computational power, and bandwidth limitations imposed by such networks, place tight constraints on the transmission capacities of the individual nodes. In this paper we investigate the effects of subtractive dithered uniform quantisation in PDMM for the synchronous distributed averaging problem. This is done by deriving expressions for the mean squared error (MSE) that include quantisation noise. Also, the required data rate for quantised PDMM is considered. It was found that for practical applications quantisation in PDMM can be applied with a fixed-rate quantiser, such that significant data rate reduction can be achieved, without compromising the rate of convergence.@en

In recent years, the large increase in connected devices and the data that are collected by these devices have caused a heightened interest in distributed processing. Many practical distributed networks are of heterogeneous nature, because different devices in the network can have different specifications. Because of this, it is highly desirable that algorithms operating within these networks can operate asynchronously, since in that case there is no need for clock synchronisation between the nodes, and the algorithm is not slowed down by the slowest device in the network. In this paper, we focus on the primal-dual method of multipliers (PDMM), which is a promising distributed optimisation algorithm that is suitable for distributed optimisation in heterogeneous networks. Most theoretical work that can be found in existing literature focuses on synchronous versions of PDMM. In this work, we prove the convergence of stochastic PDMM, which is a general framework that can model variations such as asynchronous PDMM and PDMM with transmission losses.@en

In this paper, we focus on the challenge of processing data generated within decentralised wireless sensor networks in a distributed manner. When the desired operations can be expressed as globally constrained separable convex optimisation problems, we show how we can convert these to extended monotropic programs and exploit Lagrangian duality to form equivalent distributed consensus problems. Such problems can be embedded in sensor network applications via existing solvers such as the alternating direction method of multipliers or the primal dual method of multipliers. We then demonstrate how this approach can be used to solve specific problems including linearly constrained quadratic problems and the classic Gaussian channel capacity maximisation problem in a distributed manner.@en

In this paper, we present a novel derivation of an existing algorithm for distributed optimization termed the primal-dual method of multipliers (PDMM). In contrast to its initial derivation, monotone operator theory is used to connect PDMM with other first-order methods such as Douglas-Rachford splitting and the alternating direction method of multipliers, thus, providing insight into its operation. In particular, we show how PDMM combines a lifted dual form in conjunction with Peaceman-Rachford splitting to facilitate distributed optimization in undirected networks. We additionally demonstrate sufficient conditions for primal convergence for strongly convex differentiable functions and strengthen this result for strongly convex functions with Lipschitz continuous gradients by introducing a primal geometric convergence bound.@en

We propose a new robust distributed linearly constrained beamformer which utilizes a set of linear equality constraints to reduce the cross power spectral density matrix to a block-diagonal form. The proposed beamformer has a convenient objective function for use in arbitrary distributed network topologies while having identical performance to a centralized implementation. Moreover, the new optimization problem is robust to relative acoustic transfer function (RATF) estimation errors and to target activity detection (TAD) errors. Two variants of the proposed beamformer are presented and evaluated in the context of multi-microphone speech enhancement in a wireless acoustic sensor network, and are compared with other state-of-the-art distributed beamformers in terms of communication costs and robustness to RATF estimation errors and TAD errors.@en

In this paper we propose a distributed reformulation of the linearly constrained minimum variance (LCMV) beamformer for use in acoustic wireless sensor networks. The proposed distributed minimum variance (DMV) algorithm, for which we demonstrate implementations for both cyclic and acyclic networks, allows the optimal beamformer output to be computed at each node without the need for sharing raw data within the network. By exploiting the low rank structure of estimated covariance matrices in time-varying noise fields, the algorithm can also provide a reduction in the total amount of data transmitted during computation when compared to centralised solutions. This is particularly true when multiple microphones are used per node. We also compare the performance of DMV with state of the art distributed beamformers and demonstrate that it achieves greater improvements in SNR in dynamic noise fields with similar transmission costs.@en

In this paper the effects of quantisation on distributed convex optimisation algorithms are explored via the lens of monotone operator theory. Specifically, by representing transmission quantisation via an additive noise model, we demonstrate how quantisation can be viewed as an instance of an inexact Krasnosel' skiľ-Mann scheme. In the case of two distributed solvers, the Alternating Direction Method of Multipliers and the Primal Dual Method of Multipliers, we further demonstrate how an adaptive quantisation scheme can be constructed to reduce transmission costs between nodes. Finally for the Gaussian channel capacity maximisation problem, we demonstrate convergence even in the presence of one-bit uniform quantisation based on the aforementioned adaptive quantisation scheme.@en

In this paper, we present a novel method for convex optimization in distributed networks called the distributed method of multipliers (DMM). The proposed method is based on a combination of a particular dual lifting and classic monotone operator splitting approaches to produce an algorithm with guaranteed asymptotic convergence in undirected networks. The proposed method allows any separable convex problem with linear constraints to be solved in undirected networks. In contrast to typical distributed approaches, the structure of the network does not restrict the types of problems that can be solved. Furthermore, the solver can be applied to general separable problems, those with separable convex objectives and constraints, via the use of an additional primal lifting approach. Finally, we demonstrate the use of DMM in solving a number of classic signal processing problems including beamforming, channel capacity maximization and portfolio optimization.@en