Uday V. Shanbhag

Nash Equilibrium Problems With Scaled Congestion Costs and Shared Constraints

We consider a class of convex Nash games where strategy sets are coupled across agents through a common constraint and payoff functions are linked via a scaled congestion cost metric. A solution to a related variational inequality problem provides a set of Nash equilibria characterized by common Lagrange multipliers for shared constraints. While this variational problem may be characterized by a non-monotone map, it is shown to admit solutions, even in the absence of restrictive compactness assumptions on strategy sets. Additionally, we show that the equilibrium is locally unique both in the primal space as well as in the larger primal-dual space. The existence statements can be generalized to accommodate a piecewise-smooth congestion metric while affine restrictions, surprisingly, lead to both existence and global uniqueness guarantees. In the second part of the technical note, we discuss distributed computation of such equilibria in monotone regimes via a distributed iterative Tikhonov regularization (ITR) scheme. Application to a class of networked rate allocation games suggests that the ITR schemes perform better than their two-timescale counterparts.

On stochastic gradient and subgradient methods with adaptive steplength sequences

Traditionally, stochastic approximation (SA) schemes have been popular choices for solving stochastic optimization problems. However, the performance of standard SA implementations can vary significantly based on the choice of the steplength sequence, and in general, little guidance is provided about good choices. Motivated by this gap, we present two adaptive steplength schemes for strongly convex differentiable stochastic optimization problems, equipped with convergence theory, that aim to overcome some of the reliance on user-specific parameters. The first scheme, referred to as a recursive steplength stochastic approximation (RSA) scheme, optimizes the error bounds to derive a rule that expresses the steplength at a given iteration as a simple function of the steplength at the previous iteration and certain problem parameters. The second scheme, termed as a cascading steplength stochastic approximation (CSA) scheme, maintains the steplength sequence as a piecewise-constant decreasing function with the reduction in the steplength occurring when a suitable error threshold is met. Then, we allow for nondifferentiable objectives but with bounded subgradients over a certain domain. In such a regime, we propose a local smoothing technique, based on random local perturbations of the objective function, that leads to a differentiable approximation of the function. Assuming a uniform distribution on the local randomness, we establish a Lipschitzian property for the gradient of the approximation and prove that the obtained Lipschitz bound grows at a modest rate with problem size. This facilitates the development of an adaptive steplength stochastic approximation framework, which now requires sampling in the product space of the original measure and the artificially introduced distribution.

Multiuser Optimization: Distributed Algorithms and Error Analysis

Traditionally, a multiuser problem is a constrained optimization problem characterized by a set of users, an objective given by a sum of user-specific utility functions, and a collection of linear constraints that couple the user decisions. The users do not share the information about their utilities, but do communicate values of their decision variables. The multiuser problem is to maximize the sum of the user-specific utility functions subject to the coupling constraints, while abiding by the informational requirements of each user. In this paper, we focus on generalizations of convex multiuser optimization problems where the objective and constraints are not separable by user and instead consider instances where user decisions are coupled, both in the objective and through nonlinear coupling constraints. To solve this problem, we consider the application of gradient-based distributed algorithms on an approximation of the multiuser problem. Such an approximation is obtained through a Tikhonov regulariza...

A Complementarity Framework for Forward Contracting Under Uncertainty

We consider a particular instance of a stochastic multi-leader multi-follower equilibrium problem in which players compete in the forward and spot markets in successive periods. Proving the existence of such equilibria has proved difficult, as has the construction of globally convergent algorithms for obtaining such points. By conjecturing a relationship between forward and spot decisions, we consider a variant of the original game and relate the equilibria of this game to a related simultaneous stochastic Nash game where forward and spot decisions are made simultaneously. We characterize the complementarity problem corresponding to the simultaneous Nash game and prove that it is indeed solvable. Moreover, we show that an equilibrium to this Nash game is a local Nash equilibrium of the conjectured variant of the multi-leader multi-follower game of interest. Numerical tests reveal that the difference between equilibrium profits between the original and constrained games are small. Under uncertainty, the equilibrium point of interest is obtainable as the solution to a stochastic mixed-complementarity problem. Based on matrix-splitting methods, a globally convergent decomposition method is suggested for such a class of problems. Computational tests show that the effort grows linearly with the number of scenarios. Further tests show that the method can address larger networks as well. Finally, some policy-based insights are drawn from utilizing the framework to model a two-settlement six-node electricity market.

Regularized Iterative Stochastic Approximation Methods for Stochastic Variational Inequality Problems

We consider a Cartesian stochastic variational inequality problem with a monotone map. Monotone stochastic variational inequalities arise naturally, for instance, as the equilibrium conditions of monotone stochastic Nash games over continuous strategy sets or multiuser stochastic optimization problems. We introduce two classes of stochastic approximation methods, each of which requires exactly one projection step at every iteration, and provide convergence analysis for each of them. Of these, the first is a stochastic iterative Tikhonov regularization method which necessitates the update of the regularization parameter after every iteration. The second method is a stochastic iterative proximal-point method, where the centering term is updated after every iteration. The Cartesian structure lends itself to constructing distributed multi-agent extensions and conditions are provided for recovering global convergence in limited coordination variants where agents are allowed to choose their steplength sequences, regularization and centering parameters independently, while meeting a suitable coordination requirement. We apply the proposed class of techniques and their limited coordination versions to a stochastic networked rate allocation problem.

On the Characterization of Solution Sets of Smooth and Nonsmooth Convex Stochastic Nash Games

Variational analysis provides an avenue for characterizing solution sets of deterministic Nash games over continuous-strategy sets. We examine whether similar statements, particularly pertaining to existence and uniqueness, may be made when player objectives are given by expectations. For instance, in deterministic regimes, a suitable coercivity condition associated with the gradient map is sufficient for the existence of a Nash equilibrium; in stochastic regimes, the application of this condition requires being able to analytically evaluate the expectation and its gradients. Our interest is in developing a framework that relies on the analysis of merely the integrands of the expectations; in the context of existence statement, we consider whether the satisfaction of a suitable coercivity condition in an almost-sure sense may lead to statements about the original stochastic Nash game. Notably, this condition also guarantees the existence of an equilibrium of the scenario-based Nash game. We consider a ran...

Distributed Computation of Equilibria in Monotone Nash Games via Iterative Regularization Techniques

We consider the development of single-timescale schemes for the distributed computation of equilibria associated with Nash games in which each player solves a convex program. Equilibria associated with such games are wholly captured by the solution set of a variational inequality. Our focus is on a class of games, termed monotone Nash games, that lead to monotone variational inequalities. Distributed extensions of standard approaches for solving such variational problems are characterized by two challenges: (1) Unless suitable assumptions (such as strong monotonicity) are imposed on the mapping arising in the specification of the variational inequality, iterative methods often require the solution of a sequence of regularized problems, a naturally two-timescale process that is harder to implement in practice. (2) Additionally, algorithm parameters for all players (such as steplengths and regularization parameters) have to be chosen centrally and communicated to all players; importantly, these parameters c...

Dynamic Competitive Equilibria in Electricity Markets

This chapter addresses the economic theory of electricity markets, viewed from an idealized competitive equilibrium setting, taking into account volatility and the physical and operational constraints inherent to transmission and generation. In a general dynamic setting, we establish many of the standard conclusions of competitive equilibrium theory: Market equilibria are efficient, and average prices coincide with average marginal costs. However, these conclusions hold only on average. An important contribution of this chapter is the explanation of the exotic behavior of electricity prices. Through theory and examples, we explain why, in the competitive equilibrium, sample-paths of prices can range from negative values, to values far beyond the “choke-up” price—which is usually considered to be the maximum price consumers are willing to pay. We also find that the variance of prices may be very large, but this variance decreases with increasing demand response.

A gossip algorithm for aggregative games on graphs

We consider a class of games, termed as aggregative games, being played over a distributed multi-agent networked system. In an aggregative game, an agents objective function is coupled through a function of the aggregate of all agents decisions. Every agent maintains an estimate of the aggregate and agents exchange this information over a connected network. We study the gossip-based distributed algorithm for the exchange of information and computation of equilibrium decisions of agents over the network. Our primary emphasis lies in proving the convergence of the algorithm under an assumption of a diminishing (agent-specific) step-size sequence. Under standard conditions, we establish the almost-sure convergence of the generated sequence to the unique equilibrium point.

Revisiting Generalized Nash Games and Variational Inequalities

Generalized Nash games with shared constraints represent an extension of Nash games in which strategy sets are coupled across players through a shared or common constraint. The equilibrium conditions of such a game can be compactly stated as a quasi-variational inequality (QVI), an extension of the variational inequality (VI). In (Eur. J. Oper. Res. 54(1):81–94, 1991), Harker proved that for any QVI, under certain conditions, a solution to an appropriately defined VI solves the QVI. This is a particularly important result, given that VIs are generally far more tractable than QVIs. However Facchinei et al. (Oper. Res. Lett. 35(2):159–164, 2007) suggested that the hypotheses of this result are difficult to satisfy in practice for QVIs arising from generalized Nash games with shared constraints. We investigate the applicability of Harker’s result for these games with the aim of formally establishing its reach. Specifically, we show that if Harker’s result is applied in a natural manner, its hypotheses are impossible to satisfy in most settings, thereby supporting the observations of Facchinei et al. But we also show that an indirect application of the result extends the realm of applicability of Harker’s result to all shared-constraint games. In particular, this avenue allows us to recover as a special case of Harker’s result, a result provided by Facchinei et al. (Oper. Res. Lett. 35(2):159–164, 2007), in which it is shown that a suitably defined VI provides a solution to the QVI of a shared-constraint game.