Latest Research in Optimization and Control

Top Research Topics in Optimization and Control

• Optimization and Control — 15 papers
• Machine Learning — 9 papers
• Statistics Theory — 9 papers
• Machine Learning — 5 papers
• Stochastic Gradient Optimization Techniques — 3 papers
• Geometric Analysis and Curvature Flows — 3 papers
• Tensor decomposition and applications — 2 papers
• Wireless Signal Modulation Classification — 2 papers
• Numerical Analysis — 2 papers
• Methodology — 2 papers

Highest-Cited Papers

1. Singular-Subspace Alignment in Norm-Constrained Asymmetric Low-Rank Updates
2. Singular-Subspace Alignment in Norm-Constrained Asymmetric Low-Rank Updates
3. The First-Order Oracle Complexity of Lipschitz Convex Optimization in Nondual Settings
4. On Finite-sample Concentration of Median of Incomplete U-Statistics
5. A Robust Perceptron Cycling Theorem and Applications
6. Revisiting Distributed Sign-Based Variance Reduction
7. Minimax optimality for sequential gradient-free minimization of smooth functions and their derivatives
8. Sparse Data Augmentation for Optimization with Provable Guarantees
9. Algorithms for adaptive and heteroskedastic linear regression at the computational threshold
10. E8 Phase‑Density Manifold Optimization for Adaptive Decision Networks — E8 Intelligence Research
11. E8 Phase‑Density Manifold Optimization for Adaptive Decision Networks — E8 Intelligence Research
12. Sharp margin-based generalization bounds for realizable SVM
13. Bridging the Gap Between Homogeneous and Heterogeneous Asynchronous Optimization Is Surprisingly Difficult
14. Near-Optimal Nonconvex Matrix Completion
15. Riemannian Bilevel Optimization with Gradient Aggregation
16. Covariance and Principal Component Analysis on Riemannian Manifolds and Graphs
17. Scaling Laws for Physics-Aware ACOPF Surrogate Learning
18. Achieving Linear Speedup with ProxSkip in Distributed Stochastic Optimization
19. Optimal Deterministic First-Order Oracle Complexity for Nonconvex-Concave Minimax Optimization
20. Finite-Time Node Separation in Recurrent Graph Neural Networks with Persistent Gaussian Perturbations