← Back to all news
arXiv Machine Learning By Rahul Vaze

Convex Optimization with Nested Evolving Feasible Sets (CONES) under Time-Varying Loss Functions

Read the original on arXiv Machine Learning →

arXiv:2609. 11207v1 Announce Type: new Abstract: Convex Optimization with Nested Evolving Feasible Sets (CONES)} was introduced in \cite{CONESVaze} where the objective function \(f\) remains fixed but the feasible region evolves over time as a nested sequence \(S_1 \supseteq S_2 \supseteq \cdots \supseteq S_T\).

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Aug 18

Convex Optimization with Nested Evolving Feasible Sets

arXiv:2605. 07386v2 Announce Type: replace Abstract: \emph{Convex Optimization with Nested Evolving Feasible Sets (CONES)} is considered where the objective function \(f\) remains fixed but the feasible region evolves over time as a nested sequence \(S_1 \supseteq S_2 \supseteq \cdots \supseteq S_T\).

By Karthick Krishna M., Haricharan Balasundaram, Rahul Vaze
benchmarks
More like this →
arXiv Machine Learning
Jul 14

Lower Bound on the Cumulative Constrained Violation for the OGD+Projection algorithm for Constrained Online Convex Optimization (COCO)

arXiv:2607. 10808v1 Announce Type: new Abstract: The problem of constrained online convex optimization is considered, where at each round, once a learner commits to an action $x_t \in \mathcal{X} \subset \mathbb{R}^d$, a convex loss function $f_t$ and a convex constraint function $g_t$ that drives the constraint $g_t(x)\le 0$ are revealed.

By Haricharan Balasundaram, Karthick Krishna Mahendran, Rahul Vaze
benchmarks
More like this →