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Found 8 papers, 2 papers with code
Credit vs. Discount-Based Congestion Pricing: A Comparison Study
Tolling, or congestion pricing, offers a promising traffic management policy for regulating congestion, but has also attracted criticism for placing outsized financial burdens on low-income users.
Dynamic Tolling in Arc-based Traffic Assignment Models
We prove that our adaptive learning and marginal pricing dynamics converge to a neighborhood of the socially optimal loads and tolls.
Arc-based Traffic Assignment: Equilibrium Characterization and Learning
Arc-based traffic assignment models (TAMs) are a popular framework for modeling traffic network congestion generated by self-interested travelers who sequentially select arcs based on their perceived latency on the network.
Scenario-Game ADMM: A Parallelized Scenario-Based Solver for Stochastic Noncooperative Games
To accommodate this, we decompose the approximated game into a set of smaller games with few constraints for each sampled scenario, and propose a decentralized, consensus-based ADMM algorithm to efficiently compute a generalized Nash equilibrium (GNE) of the approximated game.
Towards Dynamic Causal Discovery with Rare Events: A Nonparametric Conditional Independence Test
Causal phenomena associated with rare events occur across a wide range of engineering problems, such as risk-sensitive safety analysis, accident analysis and prevention, and extreme value theory.
Pursuit of a Discriminative Representation for Multiple Subspaces via Sequential Games
We consider the problem of learning discriminative representations for data in a high-dimensional space with distribution supported on or around multiple low-dimensional linear subspaces.
Simultaneous Localization and Mapping: Through the Lens of Nonlinear Optimization
This work presents an optimization-based framework that unifies these approaches, and allows users to flexibly implement different design choices, e. g., the number and types of variables maintained in the algorithm at each time.
Zeroth-Order Methods for Convex-Concave Minmax Problems: Applications to Decision-Dependent Risk Minimization
Min-max optimization is emerging as a key framework for analyzing problems of robustness to strategically and adversarially generated data.
