5465 papers
This paper proposes a co-design framework that jointly optimizes communication topology and differential privacy levels in multi-agent formation control to balance privacy protection, network connectivity, and steady-state performance.
This paper introduces and analyzes the Generalized Multiplicative Gradient (GMG) method for solving convex optimization problems over symmetric cones with non-Lipschitz gradients, establishing an convergence rate through novel theoretical results and demonstrating its superior computational complexity compared to other first-order methods across several key applications.
This paper establishes that the most general "reasonable category of strong vector spaces" admitting formal infinite sums is equivalent to the category of ultrafinite summability spaces, characterizing it as a specific orthogonal subcategory of and analyzing its monoidal closed structures in relation to other categories of topological vector spaces.
This paper establishes the Freidlin--Wentzell large deviations principle for the stochastic Cahn--Hilliard equation with small noise and proves the convergence of the one-point large deviations rate function for its spatial finite difference method by utilizing -convergence of objective functions and overcoming non-Lipschitz drift challenges through discrete interpolation inequalities.
This paper introduces a distributionally robust framework for the single airport ground holding problem under Wasserstein ambiguity sets, featuring a novel hybrid algorithm that combines Kelly's cutting plane method with the integer L-shaped method to achieve significant computational speedups while enhancing decision-making resilience against capacity distribution shifts.
This paper establishes the central limit theorem for the temporal average of the backward Euler–Maruyama method applied to stochastic ordinary differential equations with super-linearly growing drift coefficients, deriving the result through direct analysis for sub-optimal deviation orders and via a Poisson equation approach for the optimal strong order.
This paper solves the Dirichlet problem for minimal Killing graphs over non-compact domains in 3-manifolds fibered by a Killing vector field, establishing Collin-Krust type estimates, proving uniqueness results in the Heisenberg group, and demonstrating the removability of isolated singularities.
This paper proposes and analyzes a simultaneous M-estimator for detecting a change point and recovering diffusivity values in a stochastic heat equation with space-dependent diffusivity, demonstrating that the change point converges at rate while diffusivity constants converge at rate based on local spatial measurements.
This paper establishes canonical bundle formulas for fibrations of curves with arithmetic genus one in characteristic , distinguishing between separable and inseparable cases, and applies these results to prove that a klt pair with a nef anti-log canonical divisor and a relative dimension one Albanese morphism is a fiber space over its Albanese variety.
This paper employs an inverse optimization framework on NFL play-by-play data to demonstrate that coaches' historically conservative fourth-down decisions are consistent with optimizing low quantiles of future value, revealing that their risk preferences have become more tolerant over time and vary based on field position.
This paper establishes the strong convergence rate of an Euler-Maruyama numerical scheme for one-dimensional stochastic differential equations with distributional drifts in Hölder-Zygmund spaces, supported by theoretical bounds and numerical implementation.
This paper establishes that homogenisation and renormalisation commute for the two-dimensional periodic generalised parabolic Anderson model by introducing a novel solution ansatz to handle variable coefficients and demonstrating that the standard model can be constructed via para-controlled calculus without commutator estimates.
This paper constructs analogs of Khovanov-Jacobsson classes and the Rasmussen invariant for links in the boundary of smooth oriented 4-manifolds by utilizing skein lasagna modules derived from equivariant and deformed link homology, while establishing non-vanishing results, decomposition theorems, and conditions for extending functoriality to immersed cobordisms.
This paper proposes two novel single-loop zeroth-order primal-dual algorithms, ZO-PDAPG and ZO-RMPDPG, that achieve state-of-the-art iteration complexity guarantees for solving nonconvex-(strongly) concave minimax problems with coupled linear constraints under both deterministic and stochastic settings.
This paper proves a Gersten-type conjecture for étale sheaves, including étale logarithmic Hodge-Witt sheaves and -adic Tate twists, over henselian local rings of normal crossing varieties in positive characteristic, and applies this result to establish a relative version of the conjecture for -adic étale Tate twists over semistable families in mixed characteristic as well as a generalization of Artin's theorem on Brauer groups.
This paper introduces Orthonormal Data Collaboration (ODC), a method that enforces orthonormal bases to transform the alignment challenge into a closed-form Orthogonal Procrustes problem, thereby achieving orthogonal concordance, significantly reducing computational complexity, and improving accuracy without compromising privacy or communication efficiency.
This paper extends Control Lyapunov Value Functions to disturbed nonlinear systems by defining the Robust CLVF (R-CLVF) to identify the smallest robust control invariant set and guarantee exponential stabilization, while addressing computational challenges through warmstart and system decomposition techniques.
This paper introduces a complex of cochains known as -fractional charges to define the exterior product of Hölder differential forms, thereby extending the Young integral to arbitrary dimensions and codimensions.
This paper proposes and analyzes stochastic greedy algorithms (MRG, DRG, and Random-WSSA) to efficiently solve budget- and performance-constrained weak submodular sensor selection problems with robustness guarantees, demonstrating their effectiveness in Earth-observing satellite constellation applications.
This paper proposes a novel multi-task subset selection framework that achieves localized distributional robustness by introducing a relative-entropy regularization term, which is proven equivalent to maximizing a monotone composition of submodular functions and can be efficiently solved via greedy algorithms, as validated by experiments on satellite sensor selection and image summarization.