Ermin Wei

On the O(1=k) convergence of asynchronous distributed alternating Direction Method of Multipliers

We consider a network of agents that are cooperatively solving a global optimization problem, where the objective function is the sum of privately known local objective functions of the agents and the decision variables are coupled via linear constraints. Recent literature focused on special cases of this formulation and studied their distributed solution through either subgradient based methods with O(1/√k) rate of convergence (where k is the iteration number) or Alternating Direction Method of Multipliers (ADMM) based methods, which require a synchronous implementation and a globally known order on the agents. In this paper, we present a novel asynchronous ADMM based distributed method for the general formulation and show that it converges at the rate O (1=k).

Distributed Alternating Direction Method of Multipliers

We consider a network of agents that are cooperatively solving a global unconstrained optimization problem, where the objective function is the sum of privately known local objective functions of the agents. Recent literature on distributed optimization methods for solving this problem focused on subgradient based methods, which typically converge at the rate O (1/√k), where k is the number of iterations. In this paper, k we introduce a new distributed optimization algorithm based on Alternating Direction Method of Multipliers (ADMM), which is a classical method for sequentially decomposing optimization problems with coupled constraints. We show that this algorithm converges at the rate O (1/k).

A distributed Newton method for Network Utility Maximization

Most existing work uses dual decomposition and subgradient methods to solve Network Utility Maximization (NUM) problems in a distributed manner, which suffer from slow rate of convergence properties. This work develops an alternative distributed Newton-type fast converging algorithm for solving network utility maximization problems with self-concordant utility functions. By using novel matrix splitting techniques, both primal and dual updates for the Newton step can be computed using iterative schemes in a decentralized manner with limited scalar information exchange. Similarly, the stepsize can be obtained via an iterative consensus-based averaging scheme. We show that even when the Newton direction and the stepsize in our method are computed within some error (due to finite truncation of the iterative schemes), the resulting objective function value still converges superlinearly to an explicitly characterized error neighborhood. Simulation results demonstrate significant convergence rate improvement of our algorithm relative to the existing subgradient methods based on dual decomposition.

Pursuit and an evolutionary game

Pursuit is a familiar mechanical activity that humans and animals engage in—athletes chasing balls, predators seeking prey and insects manoeuvring in aerial territorial battles. In this paper, we discuss and compare strategies for pursuit, the occurrence in nature of a strategy known as motion camouflage, and some evolutionary arguments to support claims of prevalence of this strategy, as opposed to alternatives. We discuss feedback laws for a pursuer to realize motion camouflage, as well as two alternative strategies. We then set up a discrete-time evolutionary game to model competition among these strategies. This leads to a dynamics in the probability simplex in three dimensions, which captures the mean-field aspects of the evolutionary game. The analysis of this dynamics as an ascent equation solving a linear programming problem is consistent with observed behaviour in Monte Carlo experiments, and lends support to an evolutionary basis for prevalence of motion camouflage.

A Distributed Newton Method for Network Utility Maximization—Part II: Convergence

The existing distributed algorithms for network utility maximization (NUM) problems are mostly constructed using dual decomposition and first-order (gradient or subgradient) methods, which suffer from a slow rate of convergence. Part I of this paper proposed an alternative distributed Newton-type algorithm for solving NUM problems with self-concordant utility functions. For each primal iteration, this algorithm features distributed exact stepsize calculation with finite termination and decentralized computation of the dual variables using a finitely truncated iterative scheme obtained through novel matrix splitting techniques. This paper analyzes the convergence properties of a broader class of algorithms with potentially different stepsize computation schemes. In particular, we allow for errors in the stepsize computation. We show that if the error levels in the Newton direction (resulting from finite termination of dual iterations) and stepsize calculation are below a certain threshold, then the algorithm achieves local quadratic convergence rate in primal iterations to an error neighborhood of the optimal solution, where the size of the neighborhood can be explicitly characterized by the parameters of the algorithm and the error levels.

A distributed newton method for dynamic Network Utility Maximization with delivery contracts

The standard Network Utility Maximization (NUM) problem has a static formulation, which fails to capture the temporal dynamics in modern networks. This work considers a dynamic version of the NUM problem by introducing additional constraints, referred to as delivery contracts. Each delivery contract specifies the amount of information that needs to be delivered over a certain time interval for a particular source and is motivated by applications such as video streaming or webpage loading. The existing distributed algorithms for the Network Utility Maximization problems are either only applicable for the static version of the problem or rely on dual decomposition and first-order (gradient or subgradient) methods, which are slow in convergence. In this work, we develop a distributed Newton-type algorithm for the dynamic problem, which is implemented in the primal space and involves computing the dual variables at each primal step. We propose a novel distributed iterative approach for calculating the dual variables with finite termination based on matrix splitting techniques. It can be shown that if the error level in the Newton direction (resulting from finite termination of dual iterations) is below a certain threshold, then the algorithm achieves local quadratic convergence rate to an error neighborhood of the optimal solution in the primal space. Simulation results demonstrate significant convergence rate improvement of our algorithm, relative to the existing first-order methods based on dual decomposition.

On dual convergence of the distributed Newton method for Network Utility Maximization

The existing distributed algorithms for Network Utility Maximization (NUM) problems mostly rely on dual decomposition and first-order (gradient or subgradient) methods, which suffer from slow rate of convergence. Recent works [17] and [18] proposed an alternative distributed Newton-type second-order algorithm for solving NUM problems with self-concordant utility functions. This algorithm is implemented in the primal space and involves for each primal iteration computing the dual variables using a finitely terminated iterative scheme obtained through novel matrix splitting techniques. These works presented a convergence rate analysis for the primal iterations and showed that if the error level in the Newton direction (resulting from finite termination of dual iterations) is below a certain threshold, then the algorithm achieves local quadratic convergence rate to an error neighborhood of the optimal solution. This paper builds on these works and presents a convergence rate analysis for the dual iterations that enables us to explicitly compute at each primal iteration the number of dual steps that can satisfy the error level. This yields for the first time a fully distributed second order method for NUM problems with local quadratic convergence guarantee. Simulation results demonstrate significant convergence rate improvement of our algorithm, even when only one dual update is implemented per primal iteration, relative to the existing first-order methods based on dual decomposition.

Competitive equilibrium in electricity markets with heterogeneous users and price fluctuation penalty

We develop a flexible model of competitive equilibrium in electricity markets involving heterogeneous users with shiftable demand and generator with ramp-up and ramp-down costs. While the competitive equilibrium is efficient, it may feature undesirably high price volatility. To control this volatility, we introduce an explicit penalty term on the L - 2 norm of price fluctuation in the system objective. We then establish an equivalent representation of this penalized problem. Based on the new formulation, we design alternative allocation rules to reduce price fluctuation and propose a distributed market implementation thereof.

Efficiency of linear supply function bidding in electricity markets

We study the efficiency loss caused by strategic bidding behavior from power generators in electricity markets. In the considered market, the demand of electricity is inelastic, the generators submit their supply functions (i.e., the amount of electricity willing to supply given a unit price) to bid for the supply of electricity, and a uniform price is set to clear the market. We aim to understand how the total generation cost increases under strategic bidding, compared to the minimum total cost. Existing literature has answers to this question without regard to the network structure of the market. However, in electricity markets, the underlying physical network (i.e., the electricity transmission network) determines how electricity flows through the network and thus influences the equilibrium outcome of the market. Taking into account the underlying network, we prove that there exists a unique equilibrium supply profile, and derive an upper bound on the efficiency loss of the equilibrium supply profile compared to the socially optimal one that minimizes the total cost. Our upper bound provides insights on how the network topology affects the efficiency loss.

Scalable Spectrum Allocation and User Association in Networks With Many Small Cells

A scalable framework is developed to allocate radio resources across a large number of densely deployed small cells with given traffic statistics on a slow timescale. Joint user association and spectrum allocation is first formulated as a convex optimization problem by dividing the spectrum among all possible transmission patterns of active access points (APs). To improve scalability with the number of APs, the problem is reformulated using local patterns of interfering APs. To maintain global consistency among local patterns, inter-cluster interaction is characterized as hyper-edges in a hyper-graph with nodes corresponding to subcarriers allocated to APs. A scalable solution is obtained by iteratively solving a convex optimization problem for bandwidth allocation with reduced complexity and followed by a global spectrum allocation using hyper-graph coloring. Numerical results demonstrate the proposed solution for a network with 100 APs and several hundred user equipment. For a given quality of service, the proposed scheme can often increase the network capacity severalfold compared with assigning each user to the strongest AP with full-spectrum reuse.