English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We show that the max entropy algorithm can be derandomized (with respect to aparticular objective function) to give a deterministic $3/2-\epsilon$approximation algorithm for metric TSP for some $\epsilon > 10^{-36}$.  To obtain our result, we apply the method of conditional expectation to anobjective function constructed in prior work which was used to certify that theexpected cost of the algorithm is at most $3/2-\epsilon$ times the cost of anoptimal solution to the subtour elimination LP. The proof in this work involvesshowing that the expected value of this objective function can be computed inpolynomial time (at all stages of the algorithm's execution).

A (Slightly) Improved Deterministic Approximation Algorithm for Metric TSP