2196 papers
This paper provides a full characterization of the boundedness of Laplace–Carleson embeddings on and related Orlicz spaces in terms of Carleson intensity and weighted Berezin transforms, thereby establishing crucial criteria for the infinity-norm admissibility of control operators in linear diagonal semigroup systems.
This paper demonstrates that the Modified Method of Characteristics (MMOC) offers a computationally efficient and accurate alternative to Lax-Friedrichs and Lax-Wendroff schemes for the inverse design of the Doswell frontogenesis equation, despite not preserving identity conservation.
This paper establishes a comprehensive framework proving the near-equivalence between the minimax theorem for zero-sum games with bilinear payoffs and the strong duality theorem for conic linear programs, thereby extending classical results from finite-dimensional spaces to Banach spaces and unifying diverse game classes such as semi-infinite, semidefinite, and quantum games.
This paper establishes that finite-time decoupled convergence in nonlinear two-time-scale stochastic approximation is achievable under a nested local linearity assumption with appropriate step sizes, while demonstrating that the nonlinearity of the slow-time-scale update alone can destroy this convergence property.
This paper proposes a practical continuous-time reinforcement learning framework for intensity control in choice-based network revenue management that leverages event-driven structures to avoid time discretization, demonstrating superior performance and scalability compared to state-of-the-art discretization-based methods.
This paper proposes a novel, fast-converging greedy algorithm that accurately identifies the optimal number and locations of breakpoints in piecewise polynomial regression, demonstrating superior accuracy and interpretability over existing methods on both synthetic and real-world datasets.
This paper introduces a novel participatory modelling framework that engages stakeholders in energy system planning by allowing them to explore a continuum of near-optimal designs through an interactive interface, thereby revealing their prioritization of factors like emissions and costs beyond mere economic efficiency while fostering deeper understanding of complex trade-offs.
This paper proposes a fully online, batch-free covariance matrix estimator constructed solely from Newton iterates for sketched Newton methods, enabling consistent online statistical inference for model parameters in streaming data scenarios without requiring matrix factorization.
This paper proposes two accelerated decentralized algorithms, iD2A and MiD2A, based on a novel dual approach to solve constraint-coupled optimization problems with improved convergence guarantees and lower complexity compared to existing methods.
This paper theoretically establishes that gradient descent achieves a linear convergence rate for both single-layer and multi-layer Transformers with residual connections, demonstrating that these connections mitigate the ill-conditioning caused by the softmax-induced low-rank structure to enhance optimization stability.
This paper presents a robust and adaptive model predictive control framework that utilizes Gaussian Processes with contraction metrics to learn uncertain nonlinear dynamics online, thereby guaranteeing recursive feasibility, robust constraint satisfaction, and convergence for systems affected by bounded disturbances and unmodeled nonlinearities.
This paper proposes a heuristic resolution to the St. Petersburg paradox by introducing a coarse-grained addition framework where repeated aggregation of rewards eventually reaches a state of "inertness," thereby preventing unbounded growth without relying on traditional assumptions like diminishing marginal utility.
This paper introduces Zubov-Net, a novel framework for Neural ODEs that reconciles accuracy and robustness by unifying dynamics and classification through learnable Lyapunov functions and a Zubov-driven mechanism to actively align prescribed regions of attraction with true decision boundaries.
This paper proposes a hybrid solver that combines trained primal-dual optimization proxies with classical solvers and duality theory to provide certified, interpretable speed-optimality tradeoffs, achieving over 1000x speedup on large-scale economic dispatch problems while guaranteeing a maximum optimality gap of 2%.
This paper refines inconsistency thresholds for incomplete pairwise comparison matrices by demonstrating that they depend not only on matrix size and missing entries but also critically on the underlying graph structure of known comparisons, enabling more accurate error detection and real-time monitoring.
This paper presents Enhanced-FQL(), an interpretable and computationally efficient fuzzy reinforcement learning framework for continuous control that integrates novel Fuzzified Eligibility Traces and Segmented Experience Replay to achieve stable multi-step credit assignment, improved sample efficiency, and competitive performance on the Cart-Pole benchmark without relying on complex neural architectures.
This paper introduces SODACER, a novel reinforcement learning framework that combines a dual-buffer experience replay mechanism with adaptive clustering, Control Barrier Functions, and the Sophia optimizer to achieve safe, scalable, and efficient optimal control for nonlinear systems, as validated on an HPV transmission model.
This paper proposes a framework for Riemannian zeroth-order optimization on geodesically incomplete manifolds by constructing structure-preserving complete metrics and developing an intrinsic error analysis for the symmetric two-point estimator, thereby achieving convergence guarantees comparable to the complete setting while ensuring stability in practical applications like mesh optimization.
This paper introduces a neural path estimation framework based on variational inference and stochastic control to reconstruct and infer stochastic dynamical systems from noisy, nonlinear, and sparse observations by learning a generative model that maps prior path measures to filtering posterior measures.
This paper proposes a filter line search algorithm that solves a sequence of quadratic subproblems to efficiently address nonconvex sum-of-squares optimization challenges, demonstrating through benchmarks that it significantly reduces iteration counts and computation time compared to established methods.