315 papers
This paper utilizes contraction theory to establish that semicontraction in the observable space is a sufficient condition for different network structures of nonlinear dynamical systems, such as Kuramoto oscillators, to become indistinguishable when only partial nodal measurements are available.
This paper investigates the limitations of uniquely identifying network structures in linear dynamical systems from partial measurements by characterizing the space of consistent networks through observability properties, demonstrating that observing over 6% of nodes in random networks achieves approximately 99% edge classification accuracy while linking structural identifiability to the spectral properties of an augmented observability Gramian.
This paper presents experimental verification and numerical simulations demonstrating that the spontaneous symmetry breaking machine (SSBM) can effectively solve combinatorial optimization problems, including the large-scale benchmark, by leveraging its unique principle to explore extremely stable states.
This paper characterizes optimal signed linear risk-sharing rules in decentralized networks where agents can only share risks with their direct neighbors, establishing a connection between equal-risk-sharing among friends and the graph Laplacian.
This paper presents an optimized "optimize-then-refine" framework combining mixed-integer nonlinear programming with exact symbolic computation to solve Heilbronn's triangle problem for with certified global optimality in minutes, thereby proving the optimality of a 2002 configuration and deriving exact coordinates for through $9$.
This paper establishes the well-posedness, existence of optimal controls, and a complete set of first- and second-order necessary and sufficient optimality conditions for the bilinear control of a damped wave equation over an infinite time horizon on bounded spatial domains.
This paper resolves compatibility issues between set-separation and penalization approaches in optimal control by establishing conditions under which Clarke tangent cones are Quasi Differential Quotient approximating cones, and subsequently applies this result to prove that infimum gaps for strict-sense minimizers correspond to higher-order abnormality in the Maximum Principle.
This paper establishes conditions under which multi-stage Runge-Kutta methods preserve strong contractivity for infinitesimally contracting continuous-time systems, deriving coefficient-dependent criteria for explicit schemes and extending classical implicit guarantees to strong contractivity across , , and norms while ensuring unique solvability via an auxiliary dynamic system.
This paper introduces KProxNPLVM, a novel nonlinear probabilistic latent variable model that employs Wasserstein distance-based proximal relaxation to eliminate the approximation errors inherent in conventional amortized variational inference, thereby significantly improving soft sensor modeling accuracy.
This paper proposes a unified tensor fusion framework that addresses the blind fusion of hyperspectral and multispectral images by formulating it as a coupled inverse problem to jointly estimate the high-resolution target, spatial blur, and spectral response without relying on pre-trained models or prior knowledge of degradation operators.
Motivated by search behavior analysis, this paper proposes a mixed-integer convex quadratic optimization method to simultaneously estimate multiple discrete unimodal distributions under stochastic order constraints, demonstrating improved accuracy over existing approaches in low-sample regimes.
This paper introduces a quantum mechanical framework that models quantization-based optimization as a gradient-flow dissipative system transforming into the Schrödinger equation, thereby leveraging quantum tunneling to guarantee global convergence and demonstrating superior performance over conventional algorithms in both combinatorial and nonconvex continuous optimization tasks.
This paper revisits the classical two-impulse optimal rendezvous problem between elliptic orbits by employing numerical continuation to reveal that seemingly isolated solutions are actually connected members of continuous families, thereby providing a global map of the solution landscape that clarifies robustness and identifies alternative near-optimal transfers.
This paper proposes a hybrid zonotope-based framework for computing exact and scalable forward and backward reachable sets of closed-loop ReLU RNNs without unrolling, featuring a tunable relaxation scheme to balance computational complexity and approximation accuracy while providing a sufficient condition for safety certification.
This paper introduces a class of structure-preserving commutator-free Cayley integrators within the Krotov framework for quantum optimal control, which eliminate the computational cost of matrix exponentials and commutators while maintaining high-order accuracy, unitarity, and stability for both linear and nonlinear quantum systems.
This paper presents an optimized C++ implementation of the QPALM-OCP algorithm that exploits the inherent stage-wise parallelism in optimal control problems to significantly enhance computational performance through parallelization and vectorization.
This paper establishes a new on-average stability analysis for multipass Preconditioned SGD to derive generalization bounds dependent on effective dimension, revealing how mismatches between population risk curvature and gradient noise geometry can lead to suboptimal performance if preconditioning is improperly chosen.
This paper introduces a maximum-entropy random walk framework for directed hypergraphs that models both broadcasting and merging interaction mechanisms through a Kullback-Leibler divergence projection, utilizing Sinkhorn-Schrödinger iterations to infer transition kernels while analyzing ergodicity and demonstrating effectiveness on synthetic and real-world datasets.
This paper provides the first exact equilibrium characterization for finite-player continuous-time LQG games with endogenous signals by collapsing the infinite hierarchy of beliefs into a deterministic fixed point, thereby deriving an explicit information wedge that quantifies the strategic value of manipulating opponents' forecasts.
This paper establishes the asymptotic convergence and iteration complexity of block majorization-minimization algorithms for smooth nonconvex optimization problems with block constraints on Riemannian manifolds, demonstrating their broad applicability and superior performance over standard Euclidean approaches.