Time–Space Tradeoffs for SAT on Nonuniform Machines
✍ Scribed by Iannis Tourlakis
- Publisher
- Elsevier Science
- Year
- 2001
- Tongue
- English
- Weight
- 176 KB
- Volume
- 63
- Category
- Article
- ISSN
- 0022-0000
No coin nor oath required. For personal study only.
✦ Synopsis
are generalized and combined with an argument for diagonalizing over machines taking n bits of advice on inputs of length n to obtain the first nontrivial time-space lower bounds for SAT on nonuniform machines.
In particular, we show that for any a < 2 and any e > 0, SAT cannot be computed by a random access deterministic Turing machine using n a time, n o(1) space, and o(n 2 /2 -e ) advice nor by a random access deterministic Turing machine using n 1+o(1) time, n 1 -e space, and n 1 -e advice. More generally, we show that if for some e > 0 there exists a random access deterministic Turing machine solving SAT using n a time, n b space, and o(n (a+b)/2 -e ) advice, then a \ 1 2 ( `b2 +8 -b). Lower bounds for computing SAT on random access nondeterministic Turing machines taking sublinear advice are also obtained. Moreover, we show that SAT does not have NC 1 circuits of size n l+o(1) generated by a nondeterministic log-space machine taking n o(1) advice. Additionally, new separations of uniform classes are obtained. We show that for all e > 0 and all rational numbers r \ 1, DTISP(n r , n 1 -e ) is properly contained in NTIME(n r ).
📜 SIMILAR VOLUMES
We give the first nontrivial model-independent time space tradeoffs for satisfiability. Namely, we show that SAT cannot be solved in n 1+o(1) time and n 1&= space for any =>0 general random-access nondeterministic Turing machines. In particular, SAT cannot be solved deterministically by a Turing mac
We obtain the first non-trivial time-space tradeoff lower bound for functions f: {0, 1} n Q {0, 1} on general branching programs by exhibiting a Boolean function f that requires exponential size to be computed by any branching program of length (1+e) n, for some constant e > 0. We also give the firs
In this paper we propose a family of algorithms combining tree-clustering with conditioning that trade space for time. Such algorithms are useful for reasoning in probabilistic and deterministic networks as well as for accomplishing optimization tasks. By analyzing the problem structure, the user ca
Though it is common practice to treat synchronization primitives for multiprocessors as abstract data types, they are in reality machine instructions on registers. A crucial theoretical question with practical implications is the relationship between the size of the register and its computational po