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Found 18 papers, 3 papers with code
Competitive Equilibrium in Microgrids With Dynamic Loads
We establish a local energy market by defining a competitive equilibrium which balances energy and satisfies voltage constraints within the microgrid for all time.
Learning Stable Koopman Embeddings for Identification and Control
Whereas most existing works on Koopman learning do not take into account the stability or stabilizability of the model -- two fundamental pieces of prior knowledge about a given system to be identified -- in this paper, we propose new classes of Koopman models that have built-in guarantees of these properties.
Distributed Solvers for Network Linear Equations with Scalarized Compression
We then employ such a compressed consensus flow as a fundamental consensus subroutine to develop distributed continuous-time and discrete-time solvers for network linear equations, and prove their exponential convergence properties under scalar node communications.
PEBO-SLAM: Observer design for visual inertial SLAM with convergence guarantees
This paper introduces a new linear parameterization to the problem of visual inertial simultaneous localization and mapping (VI-SLAM) -- without any approximation -- for the case only using information from a single monocular camera and an inertial measurement unit.
A Matlab and CasADi-based Implementation of RICE Dynamic Game
The most widely used integrated assessment model for studying the economics of climate change is the dynamic/regional integrated model of climate and economy (DICE/RICE).
Competitive Equilibrium for Dynamic Multi-Agent Systems: Social Shaping and Price Trajectories
We shape individual preferences through a set of utility functions to guarantee the resource price at a competitive equilibrium remains socially acceptable, i. e., the price is upper-bounded by an affordability threshold.
CPnP: Consistent Pose Estimator for Perspective-n-Point Problem with Bias Elimination
The Perspective-n-Point (PnP) problem has been widely studied in both computer vision and photogrammetry societies.
Social Shaping of Dynamic Multi-Agent Systems over a Finite Horizon
By shaping these preferences and proposing a set of utility functions, we can guarantee that the optimal resource price at the competitive equilibrium always remains socially acceptable, i. e., it never violates a given threshold that indicates affordability.
Multi-agent consensus over time-invariant and time-varying signed digraphs via eventual positivity
For the time-varying case, convergence to consensus can be guaranteed by the existence of a common Lyapunov function for all the signed Laplacians.
No-regret Online Learning over Riemannian Manifolds
We consider online optimization over Riemannian manifolds, where a learner attempts to minimize a sequence of time-varying loss functions defined on Riemannian manifolds.
Learning Stable Koopman Embeddings
In this paper, we present a new data-driven method for learning stable models of nonlinear systems.
Differential Privacy with Manifold Data Dependency
In this paper, we study dataset processing mechanisms generated by linear queries in the presence of manifold data dependency.
Distributed Zeroth-Order Optimization: Convergence Rates That Match Centralized Counterpart
Zeroth-order optimization has become increasingly important in complex optimization and machine learning when cost functions are impossible to be described in closed analytical forms.
Network Learning in Quadratic Games from Fictitious Plays
Next, in view of the inherent stability and sparsity constraints for the network interaction structure, we propose a stable and sparse system identification framework for learning the interaction graph from full player action observations.
Social Shaping for Transactive Energy Systems
This paper considers the problem of shaping agent utility functions in a transactive energy system to ensure the optimal energy price at a competitive equilibrium is always socially acceptable, that is, below a prescribed threshold.
Online Convex Optimization Over Erdos-Renyi Random Networks
The regret bounds scaling with respect to $T$ match those obtained by state-of-the-art algorithms and fundamental limits in the corresponding centralized online optimization problems, e. g., $\mathcal{O}(\sqrt{T}) $ and $\mathcal{O}(\ln(T)) $ regrets are established for convex and strongly convex losses with full gradient feedback and two-points information, respectively.
Distributed Online Optimization with Long-Term Constraints
When the loss functions are strongly convex, we establish improved regret and constraint violation upper bounds in $\mathcal{O}(\log(T))$ and $\mathcal{O}(\sqrt{T\log(T)})$.
Distributed Online Linear Regression
We propose simple and natural distributed regression algorithms, involving, at each node and in each round, a local gradient descent step and a communication and averaging step where nodes aim at aligning their predictors to those of their neighbors.
