Interest in bilevel optimization has grown in recent years, partially due to
its applications to tackle challenging machine-learning problems. Several
exciting recent works have been centered around developing efficient
gradient-based algorithms that can solve bilevel optimization problems with
provable guarantees. However, the existing literature mainly focuses on bilevel
problems either without constraints, or featuring only simple constraints that
do not couple variables across the upper and lower levels, excluding a range of
complex applications. Our paper studies this challenging but less explored
scenario and develops a (fully) first-order algorithm, which we term BLOCC, to
tackle BiLevel Optimization problems with Coupled Constraints. We establish
rigorous convergence theory for the proposed algorithm and demonstrate its
effectiveness on two well-known real-world applications - hyperparameter
selection in support vector machine (SVM) and infrastructure planning in
transportation networks using the real data from the city of Seville.

我们的论文研究了具有耦合约束的双层优化问题，并开发了一种名为 BLOCC 的（完全）一阶算法，实现对这一具有挑战性但较少被探索的场景的解决。我们为所提算法建立了严格的收敛理论，并通过使用塞维利亚城市的真实数据，对 SVM 中的超参数选择和交通网络的基础设施规划这两个知名的实际应用进行了有效性验证。

基于原始 - 对偶辅助罚函数的耦合约束双层优化方法

A Primal-Dual-Assisted Penalty Approach to Bilevel Optimization with  Coupled Constraints

Over the past decade, the electric vehicle industry has experienced
unprecedented growth and diversification, resulting in a complex ecosystem. To
effectively manage this multifaceted field, we present an EV-centric knowledge
graph (EVKG) as a comprehensive, cross-domain, extensible, and open geospatial
knowledge management system. The EVKG encapsulates essential EV-related
knowledge, including EV adoption, electric vehicle supply equipment, and
electricity transmission network, to support decision-making related to EV
technology development, infrastructure planning, and policy-making by providing
timely and accurate information and analysis. To enrich and contextualize the
EVKG, we integrate the developed EV-relevant ontology modules from existing
well-known knowledge graphs and ontologies. This integration enables
interoperability with other knowledge graphs in the Linked Data Open Cloud,
enhancing the EVKG's value as a knowledge hub for EV decision-making. Using six
competency questions, we demonstrate how the EVKG can be used to answer various
types of EV-related questions, providing critical insights into the EV
ecosystem. Our EVKG provides an efficient and effective approach for managing
the complex and diverse EV industry. By consolidating critical EV-related
knowledge into a single, easily accessible resource, the EVKG supports
decision-makers in making informed choices about EV technology development,
infrastructure planning, and policy-making. As a flexible and extensible
platform, the EVKG is capable of accommodating a wide range of data sources,
enabling it to evolve alongside the rapidly changing EV landscape.

本文介绍了一种面向电动汽车的知识图谱 (EVKG), 通过整合本体论模块实现对 EVKG 的增强和上下文化，以支持 EV 技术的发展、基础设施的规划和决策制定的相关信息和分析的提供，从而提供了一个高效和有效的方法来管理复杂和多样化的 EV 行业，是一个灵活、可扩展的平台，能够容纳广泛的数据来源，确保 EVKG 与不断变化的 EV 行业同步发展。

EVKG: 互联互通的电动汽车知识图谱，为智能交通系统服务

EVKG: An Interlinked and Interoperable Electric Vehicle Knowledge Graph  for Smart Transportation System

We propose a model of interdependent scheduling games in which each player
controls a set of services that they schedule independently. A player is free
to schedule his own services at any time; however, each of these services only
begins to accrue reward for the player when all predecessor services, which may
or may not be controlled by the same player, have been activated. This model,
where players have interdependent services, is motivated by the problems faced
in planning and coordinating large-scale infrastructures, e.g., restoring
electricity and gas to residents after a natural disaster or providing medical
care in a crisis when different agencies are responsible for the delivery of
staff, equipment, and medicine. We undertake a game-theoretic analysis of this
setting and in particular consider the issues of welfare maximization,
computing best responses, Nash dynamics, and existence and computation of Nash
equilibria.

该论文提出了一个有关互相依赖调度游戏的模型，以解决在规划和协调大规模基础设施方面所面临的问题。通过博弈论分析模型，探讨该模型中的福利最大化、纳什平衡等方面的问题。