337 papers
This paper proposes a fix-and-propagate heuristic that leverages low-precision, GPU-accelerated first-order LP solutions to efficiently solve large-scale mixed-integer linear optimization problems, demonstrating superior performance over state-of-the-art commercial solvers on massive unit commitment instances.
This paper introduces a branch-and-cut algorithm that reformulates mixed-integer Nash equilibrium problems as bilevel optimization tasks to either compute a pure Nash equilibrium or prove its non-existence, with specific adaptations and finite termination guarantees for both standard and generalized settings.
This paper proposes a novel Bayesian framework that generalizes federated ADMM by leveraging variational inference duality, yielding both a theoretical unification of ADMM with Gaussian assumptions and practical, high-performance variants like Newton-like and Adam-like updates for diverse distribution families.
This paper introduces Fast Equivariant Imaging (FEI), a novel unsupervised learning framework that leverages the Augmented Lagrangian method and auxiliary Plug-and-Play denoisers to achieve a 10x training acceleration and improved generalization for deep imaging tasks like X-ray CT reconstruction and inpainting without requiring ground-truth data.
This paper proposes and experimentally validates a robust control strategy for regulating the outlet temperature of a counter-current heat exchanger by modeling it as a structured bilinear system and implementing both an output feedback controller with a state observer and a purely integral control law.
This paper proposes a continuous, time-invariant feedback control strategy that achieves almost global asymptotic stabilization to a desired location on the n-sphere while avoiding star-shaped and conic constraints by dynamically guiding the state either along geodesics or toward the antipode of unsafe regions.
This paper proposes a scalable second-order cubic-regularized Riemannian Newton algorithm for -means clustering that reformulates the problem as a smooth unconstrained optimization on a product manifold, enabling linear-time subproblem solutions and achieving faster convergence with optimal statistical accuracy compared to state-of-the-art first-order methods.
This paper demonstrates that the implicit bias of per-sample Adam on separable data can deviate from the full-batch -max-margin behavior, potentially converging to the -max-margin classifier or a data-adaptive Mahalanobis-norm margin depending on the dataset, whereas Signum consistently converges to the -max-margin regardless of batch size.
This paper presents a multiscale optimization framework for Lipschitz continuous functions that accelerates convergence and reduces computational costs by solving coarse-grid problems to warm-start fine-grid iterations, achieving provably tighter error bounds and significant speedups in applications like probability density estimation.
This paper proposes a stochastic gradient descent algorithm that dynamically maximizes revenue in a single-server queue with balking customers by using a novel Infinitesimal Perturbation Analysis procedure to estimate effective arrival rates based solely on observable joining behavior, thereby converging to the optimal price under mild regularity conditions.
This paper introduces a novel, heuristic-driven computational method for unit commitment that achieves orders-of-magnitude performance improvements over current state-of-the-art tools without requiring linearized approximations, thereby enabling the analysis of large-scale, complex power systems with volatile loads and distributed generation.
This paper establishes concentration bounds for random Euclidean combinatorial optimization problems with -costs in dimensions by combining Poincaré inequalities with robust geometric mechanisms, while proposing a conjectural transfer principle to potentially extend these results to all .
This paper proposes and validates a computationally tractable two-stage stochastic optimization framework for capacity procurement that integrates storage and renewables by modeling temporally correlated uncertainties, demonstrating that stochastic decomposition can efficiently solve large-scale problems while achieving faster convergence for optimization than for precise reliability metric estimation.
This paper introduces a highly scalable, structure-aware parallel presolve framework for arrowhead linear programs integrated into the PIPS-IPM++ solver, which significantly outperforms state-of-the-art tools like PaPILO and Gurobi in runtime while effectively reducing problem size in distributed high-performance computing environments.
This paper proposes Q-Measure-Learning, an efficient online reinforcement learning algorithm for continuous state spaces that represents the action-value function as a signed empirical measure updated via coupled stochastic approximation, achieving almost sure convergence to a kernel-smoothed Bellman fixed point with linear memory and computational complexity.
This paper demonstrates that while the No Free Lunch theorem holds under uniform sampling, algebraic reformulations of permutation-based optimization benchmarks create structured local violations that alter algorithm rankings and performance patterns, necessitating problem-aware algorithm selection.
This paper presents a rigorous framework that extends neural operators to robustly handle out-of-distribution input functions by leveraging kernel approximations and Reproducing Kernel Hilbert Space theory to ensure accurate prediction of both function values and derivatives, validated through solutions of elliptic partial differential equations on manifolds.
This paper proposes a two-step stochastic optimization framework that combines gradient and negative curvature steps with adaptive step sizes and early stopping to achieve high-probability convergence to second-order stationary points, offering complexity guarantees that match deterministic rates up to noise-dependent terms.
This paper proposes a two-time-scale design for local safety filters in networked systems that eliminates the need for global coordination by using derivative estimation, while providing explicit bounds on safety degradation relative to the time-scale parameter and estimation errors.
This paper investigates continuous-time multi-armed bandits with random intervention times, providing explicit characterizations of the optimal Gittins index for arms evolving as Lévy processes and specific subclasses like spectrally negative Lévy processes and diffusion processes.