Randomized rounding: a technique for provably good algorithms and algorithmic proofs
Combinatorica - Theory of Computing
SIAM Review
Approximate graph coloring by semidefinite programming
Journal of the ACM (JACM)
Approximating the independence number via the j -function
Mathematical Programming: Series A and B
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
A combinatorial approach to protein docking with flexible side-chains
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Heuristics for Finding Large Independent Sets, with Applications to Coloring Semi-random Graphs
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Protein side-chain packing problem: a maximum edge-weight clique algorithmic approach
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Protein structure optimization by side-chain positioning via beta-complex
Journal of Global Optimization
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Side-chain positioning is a central component of the protein structure prediction problem and has been the focus of a large body of research. The problem is NP-complete; in fact, it is even inapproximable. In practice, it is tackled by a variety of general search techniques and specialized heuristics. We investigate a new formulation of the problem as a semidefinite program. We introduce two novel rounding schemes and provide theoretical justifications for their effectiveness under various input conditions. We also present computational results on simulated data that show that our method outperforms a recently introduced linear programming approach on a wide range of inputs. Beyond the context of side-chain positioning, we are hopeful that our rounding schemes, which are very general, will be applicable elsewhere.