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Sungwook Jang

Publications and source records attributed to Sungwook Jang.

7 recordsLinked to original sources

Adjoint asymptotic multiplier ideal sheaves associated to potential triples

In this paper, we explore the geometry of potential triples $(X,Δ,D)$, which by definition consists of a pair $(X,Δ)$ and an $\mathbb{R}$-Cartier pseudoeffective divisor $D$ on $X$. We define and study the asymptotic multiplier ideal sheaf $\mathcal{J}(X,Δ,\lVert D\rVert)$ associated to a potential triple $(X,Δ,D)$. As a first main result, when $D$ is big, we prove that the condition $\mathcal{J}(X,Δ,\lVert D\rVert)=\mathcal{O}_{X}$ is equivalent to the triple $(X,Δ,D)$ being potentially klt, which is a klt analog of the pair $(X,Δ)$. We also study the closed set defined by the ideal sheaf $\mathcal{J}(X,Δ,\lVert D\rVert)$ and prove a Nadel type cohomology vanishing theorem for $\mathcal{J}(X,Δ,\lVert D\rVert)$. As an application of the main result, we prove that we can run the $(K_X+Δ+D)$-MMP with scaling of an ample divisor for a pklt triple $(X,Δ,D)$.

math.AG

Anticanonical divisor with good asymptotic base loci

In this paper, we give a characterization of Fano type varieties in terms of the asymptotic base loci of $-(K_X+Δ)$. We also show that for a potentially lc pair $(X,Δ)$, if no plc centers are contained in the augmented base locus $\mathbf{B}_{+}(-(K_X+Δ))$, then $(X,Δ)$ has a good $-(K_X+Δ)$-minimal model. This gives an analogous result of Birkar--Hu on the existence of good minimal models.

math.AG

A valuative approach to the anticanonical minimal model program

In this paper, we show that the log canonical threshold of a potentially klt triple can be computed by a quasi-monomial valuation. The notion of potential triples provides a larger and more flexible framework to work with than that of generalized pairs. Our main result can be considered as an extension to the result of Xu on klt pairs. As an application of the main result, we show that we can run the MMP on any potentially klt triples and $-(K_X+Δ)$-MMP on the potentially klt pairs.

math.AG

Anticanonical minimal models and Zariski decomposition

Birkar and Hu showed that if a pair $(X,Δ)$ is lc and $K_{X}+Δ$ admits a birational Zariski decomposition, then $(X,Δ)$ has a minimal model. Analogously, we prove that if a pair $(X,Δ)$ is pklt and $-(K_{X}+Δ)$ admits a birational Zariski decomposition, then $(X,Δ)$ has an anticanonical minimal

math.AG

On minimal model program and Zariski decomposition of potential triples

In this paper, we investigate properties of potential triples $(X,Δ,D)$ which consists of a pair $(X,Δ)$ and a pseudoeffective $\mathbb{R}$-Cartier divisor $D$. In particular, we show that if $D$ admits a birational Zariski decomposition, then one can associate a generalized pair structure to the potential triple $(X,Δ,D)$. Moreover, we can run the generalized MMP on $(K_X+Δ+D)$ as special cases. As an application, we also show that for a pklt pair $(X,Δ)$, if $-(K_X+Δ)$ admits a birational Zariski decomposition with $\mathrm{NQC}$ positive part, then there exists a $-(K_X+Δ)$-minimal model.

math.AG

ACC of plc thresholds

In this paper, we define potential log canonical threshold and prove that the set of those thresholds satisfies the ascending chain condition (ACC). We also consider collections of sequences of Fano type varieties and we study their basic properties including boundedness.

math.AG

A Reinforcement Learning Approach for Rebalancing Electric Vehicle Sharing Systems

This paper proposes a reinforcement learning approach for nightly offline rebalancing operations in free-floating electric vehicle sharing systems (FFEVSS). Due to sparse demand in a network, FFEVSS require relocation of electrical vehicles (EVs) to charging stations and demander nodes, which is typically done by a group of drivers. A shuttle is used to pick up and drop off drivers throughout the network. The objective of this study is to solve the shuttle routing problem to finish the rebalancing work in the minimal time. We consider a reinforcement learning framework for the problem, in which a central controller determines the routing policies of a fleet of multiple shuttles. We deploy a policy gradient method for training recurrent neural networks and compare the obtained policy results with heuristic solutions. Our numerical studies show that unlike the existing solutions in the literature, the proposed methods allow to solve the general version of the problem with no restrictions on the urban EV network structure and charging requirements of EVs. Moreover, the learned policies offer a wide range of flexibility resulting in a significant reduction in the time needed to rebalance the network.

cs.LG