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2292 papers in math.OC · page 8
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Unique optimistic choice enables bilevel differentiability on manifolds
Select-then-differentiate: Solving Bilevel Optimization with Manifold Lower-level Solution Sets
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Optimal control exists for Stokes-Cahn-Hilliard-Oono flows
An optimal control problem for Stokes-Cahn-Hilliard-Oono equations with regular potential
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Pre-check detects unbounded polynomial problems before solving
A Certificate of Unboundedness for Polynomial Optimization Problems
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MCMC and diffusion models yield 40 percent more feasible low-thrust trajectories
Transfer Learning of Multiobjective Indirect Low-Thrust Trajectories Using Diffusion Models and Markov Chain Monte Carlo
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Approximations give controllability to degenerate hyperbolic equations
Approximation of Degenerate Hyperbolic Equations with Interior Degeneracy and Applications to Controllability
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Token-flow optimization sets locational prices for AI services
Locational Pricing for Generative-AI Services via Token-Flow Market Clearing
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Optimizing reduced matrices on a manifold lowers H2 error
Riemannian optimal reduction for linear systems with quadratic outputs
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Random slicing with NW smoothing speeds up topological optimization
Towards Scalable Persistence-Based Topological Optimization
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Only one rule aggregates multiple Elo ratings while obeying three consistency axioms
Aggregating Elo Ratings: An Axiomatization
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Muon fails to converge on convex Lipschitz functions
Muon Does Not Converge on Convex Lipschitz Functions
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Soft penalties make stochastic paths match observed marginals
Learning Generative Dynamics with Soft Law Constraints: A McKean-Vlasov FBSDE Approach
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GP-MPC turned into exact LPV for fast quadcopter control
Efficient sparse GP-MPC with accurate mean and variance propagation applied for quadcopter flight control
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Variance reduction shortens time complexity in parallel optimization
Rennala MVR: Improved Time Complexity for Parallel Stochastic Optimization via Momentum-Based Variance Reduction
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Noiseless inverse optimization has tight O(d/T) generalization
Tight Generalization Bounds for Noiseless Inverse Optimization
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Local LMO matches PGD rates without bounded sets or curvature
Local LMO: Constrained Gradient Optimization via a Local Linear Minimization Oracle
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Hybrid framework cuts tote movements 8-30% in robot warehouses
Omni-scale Learning-based Sequential Decision Framework for Order Fulfillment of Tote-handling Robotic Systems
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Finite-time regulation achieved for uncertain Euler-Lagrange systems
On Composite Adaptive Continuous Finite-Time Control of a class of Euler-Lagrange systems
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NTK Kronecker core imposes low-rank bias on gradient descent
The Global Empirical NTK: Self-Referential Bias and Dimensionality of Gradient Descent Learning
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Stationary duality reduces composite counting to simple counting
On the Stationary Duality of Structural Composite Cardinality Optimization
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FDR splitting hits optimal rate and constant in proximal minimization
Optimal Acceleration for Proximal Minimization of the Sum of Convex and Strongly Convex Functions
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Fast splitting method hits optimal O(1/N²) rate
Optimal Acceleration for Proximal Minimization of the Sum of Convex and Strongly Convex Functions
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Finite point evaluations reconstruct convex Lipschitz functionals
Structure-Preserving Reconstruction of Convex Lipschitz Functionals on Hilbert Spaces from Finite Samples
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Sliced inner-product GW distance aligns high-dim data scalably
Sliced Inner Product Gromov-Wasserstein Distances
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Edge deletion leaves Laplacian spectrum unchanged exactly for rigid graphs
Total Conformal Rigidity in Graphs
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Nonconvexity indices for C^{1,1} functions via generalized Hessians
Local Nonconvexity Indices for \(C^{1,1}\) Functions via Generalized Hessians
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Accelerated optimizers' stability certified via SDP
A Unified Lyapunov-IQC Framework for Uniform Stability of Smooth Quadratic First-Order Accelerated Optimizers
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Transformers run preconditioned iteration to solve kernel regression
Transformers Can Implement Preconditioned Richardson Iteration for In-Context Gaussian Kernel Regression
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Transformers run preconditioned iteration for in-context kernel regression
Transformers Can Implement Preconditioned Richardson Iteration for In-Context Gaussian Kernel Regression
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Learning method scales nonlinear self-optimizing control to large processes
Generalized Global Self-Optimizing Control for Chemical Processes: Part II Objective-Guided Controlled Variable Learning Approach
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Wasserstein flow matching extends to measures over measures
Generalized Wasserstein Flow Matching: Transport Plans, Everywhere, All at Once
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Only three of fourteen transcription methods pass rocket landing validation
Transcription-Induced Failure Modes in 6-DOF Rocket Landing Trajectory Optimization
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A two-time-scale stochastic approximation algorithm for approximate distributionally…
Central Limit Theorem for Two-Time-Scale Approximate Distributionally Robust RL
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Approximate directional stationarity is necessary for local minima
Approximate directional stationarity and associated qualification conditions
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Penalty methods reach ε-KKT points for bilevel minimax problems in Õ(ε^{-4}) steps
Penalty-Based First-Order Methods for Bilevel Optimization with Minimax and Constrained Lower-Level Problems
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Quantum-inspired method tops solvers in non-convex ML tasks
Exploring the non-convexity in machine learning using quantum-inspired optimization
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Bidirectional compression scales distributed SGD with worker count
Scalable Distributed Stochastic Optimization via Bidirectional Compression: Beyond Pessimistic Limits
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Training breaks attention clusters in late Transformer layers
Training-Induced Escape from Token Clustering in a Mean-Field Formulation of Transformers
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Non-affine approvals force miscalibration in all proper scoring rules
The Endogeneity of Miscalibration: Impossibility and Escape in Scored Reporting
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Medoid gradients achieve O(1/T) convergence under infinite-variance noise
Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling
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Distributed algorithm converges for biased stochastic fixed points
Distributed Seeking for Fixed Points of Biased Stochastic Operators: A Communication-Efficient Approach
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Surrogate unifies distributed fixed-point and non-convex convergence
Distributed Seeking for Fixed Points of Biased Stochastic Operators: A Communication-Efficient Approach
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Quasi-stable polynomials form the idealizer of stable ones
The idealizer of the set of quasi-stable polynomials
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Rydberg atoms encode QUBO problems via local light shifts
A Unified Local Light-shifts Encoding For Solving Optimization Problems on a Rydberg Annealer
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Dynamic batching tames SGD variance in variational inference
SGD for Variational Inference: Tackling Unbounded Variance via Preconditioning and Dynamic Batching
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Stochastic planner cuts fuel for uncertain spacecraft paths
Stochastic Differential Dynamic Programming for Trajectory Optimization under Partial Observability
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Hi-MoE cuts perplexity 5.6% and balances experts 40% better
Hierarchical Mixture-of-Experts with Two-Stage Optimization
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Covariates adapt uncertainty sets to cut conservatism in power dispatch
Data-Driven Contextual-Aware Uncertainty Set for Robust Dispatch of Power Systems