Online Convex Optimization with Switching Cost with Only One Single Gradient Evaluation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916828944531456 |
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| author | Shah, Harsh Chandrasekhar, Purna Vaze, Rahul |
| author_facet | Shah, Harsh Chandrasekhar, Purna Vaze, Rahul |
| contents | Online convex optimization with switching cost is considered under the frugal information setting where at time $t$, before action $x_t$ is taken, only a single function evaluation and a single gradient is available at the previously chosen action $x_{t-1}$ for either the current cost function $f_t$ or the most recent cost function $f_{t-1}$. When the switching cost is linear, online algorithms with optimal order-wise competitive ratios are derived for the frugal setting. When the gradient information is noisy, an online algorithm whose competitive ratio grows quadratically with the noise magnitude is derived. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04133 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Online Convex Optimization with Switching Cost with Only One Single Gradient Evaluation Shah, Harsh Chandrasekhar, Purna Vaze, Rahul Optimization and Control Data Structures and Algorithms Machine Learning Online convex optimization with switching cost is considered under the frugal information setting where at time $t$, before action $x_t$ is taken, only a single function evaluation and a single gradient is available at the previously chosen action $x_{t-1}$ for either the current cost function $f_t$ or the most recent cost function $f_{t-1}$. When the switching cost is linear, online algorithms with optimal order-wise competitive ratios are derived for the frugal setting. When the gradient information is noisy, an online algorithm whose competitive ratio grows quadratically with the noise magnitude is derived. |
| title | Online Convex Optimization with Switching Cost with Only One Single Gradient Evaluation |
| topic | Optimization and Control Data Structures and Algorithms Machine Learning |
| url | https://arxiv.org/abs/2507.04133 |