# Convex Optimization on Empirical Risk Minimization Convex

Source: [Convex Optimization on Empirical Risk Minimization Convex](<https://www.sota2.com/research/sota/convex-optimization-on-empirical-risk-minimization-convex>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-09T07:54:21.082Z

Evaluation of Convex Optimization algorithms applied to problems characterized by Empirical Risk Minimization under convex assumptions, focusing on iteration complexity analysis.

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

| Scope | Recorded value |
| --- | --- |
| Task | Convex Optimization |
| Dataset (including any stated split) | Empirical Risk Minimization Convex |
| Metric | Iteration Complexity |
| Unit | SCALAR |
| Direction | Lower is better |
| Results loaded | 0 of 0 |
| Coverage | All results currently returned for this benchmark |

Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol.

## Metrics

- [Iteration Complexity](<https://www.sota2.com/research/sota/convex-optimization-on-empirical-risk-minimization-convex.md?metric=Iteration%20Complexity>): Lower is better; unit: SCALAR.

## Recorded results

| Method | Iteration Complexity | Result date | Report paper |
| --- | --- | --- | --- |

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