arXiv subjects

Jingming Yan

Publications and source records attributed to Jingming Yan.

9 recordsLinked to original sources

arXiv2026

Dual Enhancement of Superconductivity in FeSe/SrTiO3 via Orbital and Correlation Synergy

In iron-based superconductors, the dz2 orbital band typically resides far below the Fermi level and has not been considered to participate in Cooper pairing. Here, using monolayer FeSe/SrTiO3 as a model system, we demonstrate that tip-induced tensile strain controllably shifts the dz2 band toward the Fermi level, driving a two-stage enhancement of superconductivity. In-plane lattice expansion first enhances electronic correlation, amplifying superconductivity in the initial stage. As strain further increases, the upward-shifted dz2 band hybridizes with the dxy band, reconstructing the pairing-active d-orbital bands and inducing a secondary, stronger gap enhancement. Collectively, these two stages enlarge the superconducting gap from 17.8 to 23.6 meV. Throughout this process, invariant Fermi wave vectors confirm that the enhancement originates from band renormalization and reconstruction rather than carrier doping. Our work establishes a route to tailor superconducting states via strain-activated electronic correlations and band engineering, and reveals a previously unrecognized orbital-selective pairing mechanism with broad implications for correlated multiband superconductors.

cond-mat.supr-con↗

arXiv2026

The Computational Complexity of Team Zero-Sum Games

A celebrated consequence of the minimax theorem is that two-player zero-sum games admit a tractable equilibrium characterization. In many central applications, however, each side comprises multiple independent agents who share a common objective but cannot perfectly coordinate their actions. Such settings can be modeled as \emph{team zero-sum games}, a natural generalization of both two-player zero-sum games and potential games -- the two most well-studied classes of games in algorithmic game theory. In this paper, we settle the complexity of team zero-sum games by establishing that computing Nash equilibria is \PPAD-complete. As a result, despite the global adversarial structure, team zero-sum games are as hard as general-sum games. Our hardness result holds even when i) the precision is inverse polynomial, thereby ruling out a fully polynomial-time approximation scheme (unless $\P = \PPAD$); ii) each team consists of only two players; and iii) the underlying class of games is polymatrix. As a byproduct, we resolve the complexity of group-wise zero-sum polymatrix games, a class introduced and examined in the seminal work of Cai and Daskalakis (SODA '11), and more recently highlighted by Hollender, Maystre, and Nagarajan (ICLR '25). Moreover, we show that computing a first-order stationary point in min-max optimization is \PPAD-complete even for quadratic (multilinear) objectives. From a technical standpoint, we develop a series of team zero-sum game gadgets that allow us to simulate the breakthrough reduction of Bernasconi and Castiglioni (STOC '26). Moreover, to obtain hardness results for quadratic objectives, we make use of a general technique based on linear local approximation, which is of independent interest.

cs.GT↗

arXiv2026

On the Computational Complexity of Performative Prediction

Performative prediction captures the phenomenon where deploying a predictive model shifts the underlying data distribution. While simple retraining dynamics are known to converge linearly when the performative effects are weak ($\rho < 1$), the complexity in the regime $\rho > 1$ was hitherto open. In this paper, we establish a sharp phase transition: computing an $\epsilon$-performatively stable point is PPAD-complete -- and thus polynomial-time equivalent to Nash equilibria in general-sum games -- even when $\rho = 1 + O(\epsilon)$. This intractability persists even in the ostensibly simple setting with a quadratic loss function and linear distribution shifts. One of our key technical contributions is to extend this PPAD-hardness result to general convex domains, which is of broader interest in the complexity of variational inequalities. Finally, we address the special case of strategic classification, showing that computing a strategic local optimum is PLS-hard.

cs.LG↗

arXiv2025

Lattice-decoupled rotatable stripe-like charge order within the strange metal phase of 2M-WS2

In quantum materials, charge orders typically stabilize in specific crystallographic orientations, though their formation mechanisms may vary. Here, using low-temperature scanning tunneling microscopy (STM), we discover a lattice-decoupled rotatable stripe-like charge order coexisting with superconductivity in 2M-WS2. The charge order manifests five distinct orientations across different sample regions, yet maintains an identical wavelength. This directional decoupling from host lattice challenges existing paradigms. First-principles calculations of phonon spectra and nesting function fail to explain the ordering mechanism. Intriguingly, the transition temperature of the charge orders exhibits spatial variations (21-46 K), coinciding with the temperature range of the recently reported strange metal phase in this material. This correlation suggests that the interplay between strong electronic correlations and electron-phonon coupling must be critically evaluated to elucidate the emergence of this unconventional charge order.

cond-mat.supr-con↗

arXiv2025

The Complexity of Finding Local Optima in Contrastive Learning

Contrastive learning is a powerful technique for discovering meaningful data representations by optimizing objectives based on $\textit{contrastive information}$, often given as a set of weighted triplets $\{(x_i, y_i^+, z_{i}^-)\}_{i = 1}^m$ indicating that an "anchor" $x_i$ is more similar to a "positive" example $y_i$ than to a "negative" example $z_i$. The goal is to find representations (e.g., embeddings in $\mathbb{R}^d$ or a tree metric) where anchors are placed closer to positive than to negative examples. While finding $\textit{global}$ optima of contrastive objectives is $\mathsf{NP}$-hard, the complexity of finding $\textit{local}$ optima -- representations that do not improve by local search algorithms such as gradient-based methods -- remains open. Our work settles the complexity of finding local optima in various contrastive learning problems by proving $\mathsf{PLS}$-hardness in discrete settings (e.g., maximize satisfied triplets) and $\mathsf{CLS}$-hardness in continuous settings (e.g., minimize Triplet Loss), where $\mathsf{PLS}$ (Polynomial Local Search) and $\mathsf{CLS}$ (Continuous Local Search) are well-studied complexity classes capturing local search dynamics in discrete and continuous optimization, respectively. Our results imply that no polynomial time algorithm (local search or otherwise) can find a local optimum for various contrastive learning problems, unless $\mathsf{PLS}\subseteq\mathsf{P}$ (or $\mathsf{CLS}\subseteq \mathsf{P}$ for continuous problems). Even in the unlikely scenario that $\mathsf{PLS}\subseteq\mathsf{P}$ (or $\mathsf{CLS}\subseteq \mathsf{P}$), our reductions imply that there exist instances where local search algorithms need exponential time to reach a local optimum, even for $d=1$ (embeddings on a line).

cs.LG↗

arXiv2025

Topological surface states in {\gamma}-PtBi$_2$ evidenced by scanning tunneling microscopy

For the application of topological materials, the specific location of their topological surface states with respect to the Fermi level are important. {\gamma}-PtBi2 has been demonstrated to be a Weyl semimetal possessing superconducting Fermi arcs by photoemission spectroscopy. However, the evidence of its topological surface states is lacking by scanning tunneling microscopy (STM), which should be rather sensitive to detect the surface states. Here, we show multiple STM evidences for the existence of topological surface states in {\gamma}-PtBi2. We observe not only the step-edge and screw dislocation induced quasiparticle interference fringes, originating from the electron scatterings between the Fermi arcs of {\gamma}-PtBi2, but also the back-scattering prohibition related to the spin-flip process, which is the direct evidence for the topological nature of the surface states. Moreover, we demonstrate that the topological surface states are precisely located over a narrow energy range near the Fermi level, within which sharply enhanced intensity and slow spatial decay of quasiparticle interference are observed.

cond-mat.supr-con↗

arXiv2025

Interfacial superconductivity and a Se-vacancy ordered insulating phase in the FeSe/PbOx heterostructures

The discovery of high-temperature superconductivity in FeSe/SrTiO3 has sparked significant interests in exploring new superconducting systems with engineered interfaces. Here, using molecular beam epitaxy growth, we successfully fabricate FeSe/PbOx heterostructures and discover superconductivities in three different monolayer FeSe-related interfaces. We observe superconducting gaps of 13~14 meV in the monolayer FeSe films grown on two different phases of PbOx. Moreover, we discover a new insulating Fe10Se9 phase with an ordered $\sqrt{5}\times\sqrt{5}$ Se-vacancy structure. Our first-principles calculation suggests that this new insulating phase originates from electronic correlation. Intriguingly, an additional monolayer FeSe film grown on the insulating Fe10Se9 also exhibits superconductivity with the gap size of 5 meV. Our results suggest that the work function differences between the monolayer FeSe and the substrates, which can induce band bending and charge transfer, are crucial for the interfacial superconductivity.

cond-mat.supr-con↗

arXiv2025

The Complexity of Symmetric Equilibria in Min-Max Optimization and Team Zero-Sum Games

We consider the problem of computing stationary points in min-max optimization, with a particular focus on the special case of computing Nash equilibria in (two-)team zero-sum games. We first show that computing $\epsilon$-Nash equilibria in $3$-player \emph{adversarial} team games -- wherein a team of $2$ players competes against a \emph{single} adversary -- is \textsf{CLS}-complete, resolving the complexity of Nash equilibria in such settings. Our proof proceeds by reducing from \emph{symmetric} $\epsilon$-Nash equilibria in \emph{symmetric}, identical-payoff, two-player games, by suitably leveraging the adversarial player so as to enforce symmetry -- without disturbing the structure of the game. In particular, the class of instances we construct comprises solely polymatrix games, thereby also settling a question left open by Hollender, Maystre, and Nagarajan (2024). We also provide some further results concerning equilibrium computation in adversarial team games. Moreover, we establish that computing \emph{symmetric} (first-order) equilibria in \emph{symmetric} min-max optimization is \textsf{PPAD}-complete, even for quadratic functions. Building on this reduction, we further show that computing symmetric $\epsilon$-Nash equilibria in symmetric, $6$-player ($3$ vs. $3$) team zero-sum games is also \textsf{PPAD}-complete, even for $\epsilon = \text{poly}(1/n)$. As an immediate corollary, this precludes the existence of symmetric dynamics -- which includes many of the algorithms considered in the literature -- converging to stationary points. Finally, we prove that computing a \emph{non-symmetric} $\text{poly}(1/n)$-equilibrium in symmetric min-max optimization is \textsf{FNP}-hard.

cs.GT↗

arXiv2024

Learning Equilibria in Adversarial Team Markov Games: A Nonconvex-Hidden-Concave Min-Max Optimization Problem

We study the problem of learning a Nash equilibrium (NE) in Markov games which is a cornerstone in multi-agent reinforcement learning (MARL). In particular, we focus on infinite-horizon adversarial team Markov games (ATMGs) in which agents that share a common reward function compete against a single opponent, the adversary. These games unify two-player zero-sum Markov games and Markov potential games, resulting in a setting that encompasses both collaboration and competition. Kalogiannis et al. (2023a) provided an efficient equilibrium computation algorithm for ATMGs which presumes knowledge of the reward and transition functions and has no sample complexity guarantees. We contribute a learning algorithm that utilizes MARL policy gradient methods with iteration and sample complexity that is polynomial in the approximation error $\epsilon$ and the natural parameters of the ATMG, resolving the main caveats of the solution by (Kalogiannis et al., 2023a). It is worth noting that previously, the existence of learning algorithms for NE was known for Markov two-player zero-sum and potential games but not for ATMGs. Seen through the lens of min-max optimization, computing a NE in these games consists a nonconvex-nonconcave saddle-point problem. Min-max optimization has received extensive study. Nevertheless, the case of nonconvex-nonconcave landscapes remains elusive: in full generality, finding saddle-points is computationally intractable (Daskalakis et al., 2021). We circumvent the aforementioned intractability by developing techniques that exploit the hidden structure of the objective function via a nonconvex-concave reformulation. However, this introduces the challenge of a feasibility set with coupled constraints. We tackle these challenges by establishing novel techniques for optimizing weakly-smooth nonconvex functions, extending the framework of (Devolder et al., 2014).

cs.GT↗