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Data Party THU
Data Party THU
Aug 13, 2025 · Artificial Intelligence

How Dual Adaptivity Powers Universal Algorithms to Minimize Adaptive Regret

This article reviews the recent work by Zhou Zhihua’s team at Nanjing University on dual‑adaptivity universal algorithms for online convex optimization, introducing a meta‑expert framework, the UMA2 and UMA3 methods, and extending them to online composite optimization with strong adaptive‑regret guarantees.

Online Learningadaptive regretconvex optimization
0 likes · 10 min read
How Dual Adaptivity Powers Universal Algorithms to Minimize Adaptive Regret
Hulu Beijing
Hulu Beijing
Jan 2, 2018 · Fundamentals

Master Classic Optimization Algorithms: Direct vs Iterative Methods Explained

This article introduces classic optimization algorithms, distinguishing direct methods that require convexity and closed‑form solutions from iterative first‑ and second‑order methods, and explains their applicability, underlying theory, and key references for solving smooth unconstrained problems.

Newton's methodalgorithm fundamentalsconvex optimization
0 likes · 8 min read
Master Classic Optimization Algorithms: Direct vs Iterative Methods Explained
Hulu Beijing
Hulu Beijing
Nov 14, 2017 · Artificial Intelligence

Are Projected Points Still Linearly Separable? SVM Insight & Proof

This article examines whether points from two linearly separable classes remain separable after being projected onto the SVM decision hyperplane, providing geometric and convex‑optimization proofs along with illustrative diagrams and references for deeper study.

convex optimizationhyperplane projectionlinear separability
0 likes · 7 min read
Are Projected Points Still Linearly Separable? SVM Insight & Proof