Improved Approximation Algorithms for Maximum Cut and Satisfiability Problems Using Semidefinite Programming¶
Goemans, M. X., & Williamson, D. P. (1995). Improved Approximation Algorithms for Maximum Cut and Satisfiability Problems Using Semidefinite Programming. Journal of the ACM, 42(6), 1115-1145.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Domain-specific¶
- Randomized Rounding
- Goemans and Williamson's MAX CUT algorithm, for example, samples a random direction and separates relaxation vectors by the signs of their inner products with that direction.
This sourceThe primary paper verifies randomized hyperplane rounding of an SDP relaxation and the expected `>0.87856` MAX CUT guarantee.
- Goemans and Williamson's MAX CUT algorithm, for example, samples a random direction and separates relaxation vectors by the signs of their inner products with that direction.
Verification¶
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Does it back the claim? Not recorded. The single citation of this work carries no recorded support check.
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Links previously used in the corpus¶
Before the registry existed this work was also linked 1 other way.
Registry ID ref:932b2f7510ff · see in the full table