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Explore the stability of Kerr black holes in general relativity, focusing on recent advancements for small angular momentum cases.
Explore nonlinear waves and relativity through the modified energy method for a nonlocal quasilinear wave equation in this advanced mathematics lecture.
Explore a smooth solution to the linear Schrödinger equation on a 2D torus, examining logarithmic H^1 Sobolev norm growth and infinite-time blowup using adapted CKSTT techniques.
Explore dispersive properties of 2D Dirac operators with Aharonov-Bohm potentials, examining flow components and deducing sharp Strichartz estimates for advanced mathematical analysis.
Explore infinite sequences of static spherically symmetric wormhole solutions in SU(2) Einstein-Yang-Mills equations, featuring phantom field "hauntings" in traversable tunnels between universes.
Explore high-order proximal operators and Moreau envelopes in nonconvex optimization, examining properties, algorithms, and convergence analysis for advanced mathematical applications.
Explore new algorithms for smooth multiobjective optimization using linear programming to find decreasing directions, applicable to various constraint types and yielding substationary points.
Explore advanced coordinate descent methods for complex optimization problems, covering nonseparable and nonsmooth functions, random proximal gradients, and smooth approximations.
Explore joint optimization of specialized models for heterogeneous data using the sum-of-minimum approach. Learn algorithm design, performance bounds, and applications in various machine learning tasks.
Explore Kurdyka-Łojasiewicz exponents in L1-regularized optimization, focusing on Hadamard difference parametrization and its impact on local convergence rates of gradient methods.
Explore acceleration mechanisms in optimization and machine learning, aiming to unify diverse approaches for a comprehensive mathematical theory.
Explore a novel Newton-type algorithm for nonsmooth optimization problems, focusing on difference programming and its applications in structured optimization and practical problem-solving.
Explore advanced optimization techniques for constrained min-max problems, focusing on inexact fixed-point iterations and stochastic algorithms for nonconvex-nonconcave scenarios.
Explore the relationship between Bakry-Émery curvature-dimension and Kato condition on Ricci curvature, uncovering insights on Kato limit spaces and Riemannian manifolds.
Explore inertial algorithms with Tikhonov regularization for optimization problems, examining strong convergence to minimum norm solutions and fast convergence rates for objective function values.
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