Introduction to Algorithms
A deterministic (2 - 2/(k+ 1))n algorithm for k-SAT based on local search
Theoretical Computer Science
A Space-Efficient Randomized DNA Algorithm for k-SAT
DNA '00 Revised Papers from the 6th International Workshop on DNA-Based Computers: DNA Computing
A DNA-Based Random Walk Method for Solving k-SAT
DNA '00 Revised Papers from the 6th International Workshop on DNA-Based Computers: DNA Computing
An Improved Exponential-Time Algorithm for k-SAT
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
A Probabilistic Algorithm for k-SAT and Constraint Satisfaction Problems
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
How efficiently can room at the bottom be traded away for speed at the top?
Natural Computing: an international journal
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Given exponential 2n space, we know that an Adleman-Lipton [1,9] computation can decide many hard problems - such as boolean formula and boolean circuit evaluation - in a number of steps that is linear in the problem size n. We wish to better understand how to design biomolecular algorithms that trade away "weakly exponential" 2n/c, c 1, space to achieve low running times and analyze the efficiency of their space-time utilization relative to that of their best extant classical/biomolecular counterparts. We present deterministic and probabilistic parallel algorithms for the Covering Code Creation and k-SAT problems which are based on the biomolecular setting as abstracted by a randomized framework that augments that of the sticker model of Roweis et al. [13]. We illustrate the power of the randomized framework by analyzing the space-time efficiency of these biomolecular algorithms relative to the best extant classical deterministic/probabilistic algorithms [6,14] which inspired ours. This work points to the proposed randomized sticker model as a logical tool of independent interest.