arXiv subjects

Sergio Grammatico

Publications and source records attributed to Sergio Grammatico.

At least 55 records · Page 3Linked to original sources

arXiv2023

Stability of singularly perturbed hybrid systems with restricted systems evolving on boundary layer manifolds

We present a singular perturbation theory applicable to systems with hybrid boundary layer systems and hybrid reduced systems {with} jumps from the boundary layer manifold. First, we prove practical attractivity of an adequate attractor set for small enough tuning parameters and sufficiently long time between almost all jumps. Second, under mild conditions on the jump mapping, we prove semi-global practical asymptotic stability of a restricted attractor set. Finally, for certain classes of dynamics, we prove semi-global practical asymptotic stability of the restricted attractor set for small enough tuning parameters and sufficiently long period between almost all jumps of the slow states only.

math.OC↗

arXiv2023

A discrete-time averaging theorem and its application to zeroth-order Nash equilibrium seeking

In this paper we present an averaging technique applicable to the design of zeroth-order Nash equilibrium seeking algorithms. First, we propose a multi-timescale discrete-time averaging theorem that requires only that the equilibrium is semi-globally practically stabilized by the averaged system, while also allowing the averaged system to depend on ``fast" states. Furthermore, sequential application of the theorem is possible, which enables its use for multi-layer algorithm design. Second, we apply the aforementioned averaging theorem to prove semi-global practical convergence of the zeroth-order information variant of the discrete-time projected pseudogradient descent algorithm, in the context of strongly monotone, constrained Nash equilibrium problems. Third, we use the averaging theory to prove the semi-global practical convergence of the asynchronous pseudogradient descent algorithm to solve strongly monotone unconstrained Nash equilibrium problems. Lastly, we apply the proposed asynchronous algorithm to the connectivity control problem in multi-agent systems.

math.OC↗

arXiv2023

Bilateral Peer-to-Peer Energy Trading via Coalitional Games

In this paper, we propose a bilateral peer-to-peer (P2P) energy trading scheme under single-contract and multi-contract market setups, both as an assignment game, and a special class of coalitional games. {The proposed market formulation allows for efficient computation of a market equilibrium while keeping the desired economic properties offered by the coalitional games. Furthermore, our market model allows buyers to have heterogeneous preferences (product differentiation) over the energy sellers, which can be economic, social, or environmental. To address the problem of scalability in coalitional games, we design a novel distributed negotiation mechanism that utilizes the geometric structure of the equilibrium solution to improve the convergence speed. Our algorithm enables market participants (prosumers) to reach a consensus on a set of ``stable" and ``fair" bilateral contracts which encourages prosumer participation.} The negotiation process is executed with virtually minimal information requirements on a time-varying communication network that in turn preserves privacy. We use operator-theoretic tools to rigorously prove its convergence. Numerical simulations illustrate the benefits of our negotiation protocol and show that the average execution time of a negotiation step is much faster than the benchmark.

cs.GT↗

arXiv2023

Online coalitional games for real-time payoff distribution with applications to energy markets

Motivated by the markets operating on fast time scales, we present a framework for online coalitional games with time-varying coalitional values and propose real-time payoff distribution mechanisms. Specifically, we design two online distributed algorithms to track the Shapley value and the core, the two most widely studied payoff distribution criteria in coalitional game theory. We show that the payoff distribution trajectory resulting from our proposed algorithms converges to a neighborhood of the time-varying solutions. We adopt an operator-theoretic perspective to show the convergence of our algorithms. Numerical simulations of a real-time local electricity market and cooperative energy forecasting market illustrate the performance of our algorithms: {the difference between online payoffs and static payoffs (Shapley and the core) to the participants is little; online algorithms considerably improve the scalability of the mechanism with respect to the number of market participants.

eess.SY↗

arXiv2022

A two-stage approach for a mixed-integer economic dispatch game in integrated electrical and gas distribution systems

We formulate for the first time the economic dispatch problem in an integrated electrical and gas distribution system as a game equilibrium problem between distributed prosumers. Specifically, by approximating the non-linear gas-flow equations either with a mixed-integer second order cone or a piece-wise affine model and by assuming that electricity and gas prices depend linearly on the total consumption we obtain a potential mixed-integer game. To compute an approximate generalized Nash equilibrium, we propose an iterative two-stage method that exploits a problem convexification and the gas flow models. We quantify the quality of the computed solution and perform a numerical study to evaluate the performance of our method.

math.OC↗

arXiv2022

A Market for Trading Forecasts: A Wagering Mechanism

In many areas of industry and society, e.g., energy, healthcare, logistics, agents collect vast amounts of data that they deem proprietary. These data owners extract predictive information of varying quality and relevance from data depending on quantity, inherent information content, and their own technical expertise. Aggregating these data and heterogeneous predictive skills, which are distributed in terms of ownership, can result in a higher collective value for a prediction task. In this paper, we envision a platform for improving predictions via implicit pooling of private information in return for possible remuneration. Specifically, we design a wagering-based forecast elicitation market platform, where a buyer intending to improve their forecasts posts a prediction task, and sellers respond to it with their forecast reports and wagers. This market delivers an aggregated forecast to the buyer (pre-event) and allocates a payoff to the sellers (post-event) for their contribution. We propose a payoff mechanism and prove that it satisfies several desirable economic properties, including those specific to electronic platforms. Furthermore, we discuss the properties of the forecast aggregation operator and scoring rules to emphasize their effect on the sellers' payoff. Finally, we provide numerical examples to illustrate the structure and properties of the proposed market platform.

econ.TH↗

arXiv2022

Approximate solutions to the optimal flow problem of multi-area integrated electrical and gas systems

We formulate the optimal flow problem in a multi-area integrated electrical and gas system as a mixed-integer optimization problem by approximating the non-linear gas flows with piece-wise affine functions, thus resulting in a set of mixed-integer linear constraints. For its solution, we propose a novel algorithm that consists in one stage for solving a convexified problem and a second stage for recovering a mixed-integer solution. The latter exploits the gas flow model and requires solving a linear program. We provide an optimality certificate for the computed solution and show the advantages of our algorithm compared with respect to the state-of-the-art method via numerical simulations.

math.OC↗

arXiv2022

Nash equilibrium seeking under partial decision information: Monotonicity, smoothness and proximal-point algorithms

We address Nash equilibrium problems in a partial-decision information scenario, where each agent can only exchange information with some neighbors, while its cost function possibly depends on the strategies of all agents. We characterize the relation between several monotonicity and smoothness conditions postulated in the literature. Furthermore, we prove convergence of a preconditioned proximal point algorithm, under a restricted monotonicity property that allows for a non-Lipschitz, non-continuous game mapping.

math.OC↗

arXiv2022

Bregman algorithms for mixed-strategy generalized Nash equilibrium seeking in a class of mixed-integer games

We consider the problem of computing a mixed-strategy generalized Nash equilibrium (MS-GNE) for a class of games where each agent has both continuous and integer decision variables. Specifically, we propose a novel Bregman forward-reflected-backward splitting and design distributed algorithms that exploit the problem structure. Technically, we prove convergence to a variational MS-GNE under mere monotonicity and Lipschitz continuity assumptions, which are typical of continuous GNE problems. Finally, we show the performance of our algorithms via numerical experiments.

math.OC↗

arXiv2022

Game-theoretical trajectory planning enhances social acceptability for humans

Since humans and robots are increasingly sharing portions of their operational spaces, experimental evidence is needed to ascertain the safety and social acceptability of robots in human-populated environments. Although several studies have aimed at devising strategies for robot trajectory planning to perform \emph{safe} motion in populated environments, a few efforts have \emph{measured} to what extent a robot trajectory is \emph{accepted} by humans. Here, we present a navigation system for autonomous robotics that ensures safety and social acceptability of robotic trajectories. We overcome the typical reactive nature of state-of-the-art trajectory planners by leveraging non-cooperative game theory to design a planner that encapsulates human-like features of preservation of a vital space, recognition of groups, sequential and strategized decision making, and smooth obstacle avoidance. Social acceptability is measured through a variation of the Turing test administered in the form of a survey questionnaire to a pool of 691 participants. Comparison terms for our tests are a state-of-the-art navigation algorithm (Enhanced Vector Field Histogram, VFH) and purely human trajectories. While all participants easily recognized the non-human nature of VFH-generated trajectories, the distinction between game-theoretical trajectories and human ones were hardly revealed. These results mark a strong milestone toward the full integration of robots in social environments.

cs.RO↗

arXiv2022

Operationally-Safe Peer-to-Peer Energy Trading in Distribution Grids: A Game-Theoretic Market-Clearing Mechanism

In future distribution grids, prosumers (i.e., energy consumers with storage and/or production capabilities) will trade energy with each other and with the main grid. To ensure an efficient and safe operation of energy trading, in this paper, we formulate a peer-to-peer energy market of prosumers as a generalized aggregative game, in which a network operator is only responsible for the operational constraints of the system. We design a distributed market-clearing mechanism with convergence guarantee to an economically-efficient and operationally-safe configuration (i.e., a variational generalized Nash equilibrium). Numerical studies on the IEEE 37-bus testcase show the scalability of the proposed approach and suggest that active participation in the market is beneficial for both prosumers and the network operator.

eess.SY↗

arXiv2022

Optimal selection and tracking of generalized Nash equilibria in monotone games

A fundamental open problem in monotone game theory is the computation of a specific generalized Nash equilibrium (GNE) among all the available ones, e.g. the optimal equilibrium with respect to a system-level objective. The existing GNE seeking algorithms have in fact convergence guarantees toward an arbitrary, possibly inefficient, equilibrium. In this paper, we solve this open problem by leveraging results from fixed-point selection theory and in turn derive distributed algorithms for the computation of an optimal GNE in monotone games. We then extend the technical results to the time-varying setting and propose an algorithm that tracks the sequence of optimal equilibria up to an asymptotic error, whose bound depends on the local computational capabilities of the agents.

eess.SY↗

arXiv2021

Fast generalized Nash equilibrium seeking under partial-decision information

We address the generalized Nash equilibrium seeking problem in a partial-decision information scenario, where each agent can only exchange information with some neighbors, although its cost function possibly depends on the strategies of all agents. The few existing methods build on projected pseudo-gradient dynamics, and require either double-layer iterations or conservative conditions on the step sizes. To overcome both these flaws and improve efficiency, we design the first fully-distributed single-layer algorithms based on proximal best-response. Our schemes are fixed-step and allow for inexact updates, which is crucial for reducing the computational complexity. Under standard assumptions on the game primitives, we establish convergence to a variational equilibrium (with linear rate for games without coupling constraints) by recasting our algorithms as proximal-point methods, opportunely preconditioned to distribute the computation among the agents. Since our analysis hinges on a restricted monotonicity property, we also provide new general results that significantly extend the domain of applicability of proximal-point methods. Besides, the operator-theoretic approach favors the implementation of provably correct acceleration schemes that can further improve the convergence speed. Finally, the potential of our algorithms is demonstrated numerically, revealing much faster convergence with respect to projected pseudo-gradient methods and validating our theoretical findings.

math.OC↗

arXiv2021

Convergence of sequences: a survey

Convergent sequences of real numbers play a fundamental role in many different problems in system theory, e.g., in Lyapunov stability analysis, as well as in optimization theory and computational game theory. In this survey, we provide an overview of the literature on convergence theorems and their connection with Fejer monotonicity in the deterministic and stochastic settings, and we show how to exploit these results.

math.OC↗

arXiv2021

Forward-Backward algorithms for stochastic Nash equilibrium seeking in restricted strongly and strictly monotone games

We study stochastic Nash equilibrium problems with expected valued cost functions whose pseudogradient satisfies restricted monotonicity properties which hold only with respect to the solution. We propose a forward-backward algorithm and prove its convergence under restricted strong monotonicity, restricted strict monotonicity and restricted cocoercivity of the pseudogradient mapping. To approximate the expected value, we use either a finite number of samples and a vanishing step size or an increasing number of samples with a constant step. Numerical simulations show that our proposed algorithm might be faster than the available algorithms.

math.OC↗

arXiv2021

Learning generalized Nash equilibria in monotone games: A hybrid adaptive extremum seeking control approach

In this paper, we solve the problem of learning a generalized Nash equilibrium (GNE) in merely monotone games. First, we propose a novel continuous semi-decentralized solution algorithm without projections that uses first-order information to compute a GNE with a central coordinator. As the second main contribution, we design a gain adaptation scheme for the previous algorithm in order to alleviate the problem of improper scaling of the cost functions versus the constraints. Third, we propose a data-driven variant of the former algorithm, where each agent estimates their individual pseudogradient via zeroth-order information, namely, measurements of their individual cost function values. Finally, we apply our method to a perturbation amplitude optimization problem in oil extraction engineering.

eess.SY↗

arXiv2021

An extremum seeking algorithm for monotone Nash equilibrium problems

In this paper we consider the problem of finding a Nash equilibrium (NE) via zeroth-order feedback information in games with merely monotone pseudogradient mapping. Based on hybrid system theory, we propose a novel extremum seeking algorithm which converges to the set of Nash equilibria in a semi-global practical sense. Finally, we present two simulation examples. The first shows that the standard extremum seeking algorithm fails, while ours succeeds in reaching NE. In the second, we simulate an allocation problem with fixed demand.

eess.SY↗

arXiv2021

Stochastic generalized Nash equilibrium seeking in merely monotone games

We solve the stochastic generalized Nash equilibrium (SGNE) problem in merely monotone games with expected value cost functions. Specifically, we present the first distributed SGNE seeking algorithm for monotone games that requires one proximal computation (e.g., one projection step) and one pseudogradient evaluation per iteration. Our main contribution is to extend the relaxed forward-backward operator splitting by Malitsky (Mathematical Programming, 2019) to the stochastic case and in turn to show almost sure convergence to a SGNE when the expected value of the pseudogradient is approximated by the average over a number of random samples.

math.OC↗