This is a survey of space-bounded probabilistic computation, summarizing the present state of knowledge about the relationships between the various complexity classes associated with such computation. The survey especially emphasizes recent progress in the construction of pseudorandom generators

We now discuss derandomization of space-bounded algorithms. Here non-trivial results can be shown without making any unproven assumptions, in contrast to what is currently known for derandomizing time-bounded algorithms. We show first that1 BPL ⊆ SPACE(log 2 n) and then improve the analysis

Pseudorandom generators are constructed which convert O(SlogR) truly random bits to R bits that appear random to any algorithm that runs in SPACE(S). In particular, any randomized polynomial time algorithm that runs in space S can be simulated using only O(Slogn) random bits. An application

is Tp = O(TI/P + Tm), where TI is the minimum serial eze-cution time of the multithreaded computation and T, is the minimum ezecution time with an infinite number of processors. Moreover, the space Sp required by the execution satisfies Sp 5 SIP. We also show that the ezpected total communication

We give a general complexity classification scheme for monotone computation, including monotone space-bounded and Turing machine models not previously considered. We propose monotone complexity classes including mAC i , mNC i , mLOGCFL, mBWBP , mL, mNL, mP , mBPP and mNP . We define a

calculation. When tested on a common data set of word pair similarity ratings, the proposed approach outperforms other computational models. It gives the highest correlation value (r = 0.828) with a benchmark based on human similarity judgements, whereas an upper bound (r = 0.885) is observed when human

, the Cilk scheduler achieves space, time, and communication bounds all within a constant factor of optimal. The Cilk rmrtime system currently runs on the Connection Machine CM5 MPP, the Intel Paragon MPP, the Silicon Graphics Power Challenge SMP, and the MIT Phish network of workstations. Applications

be expressed as a linear-threshold algorithm. A primary advantage of this algorithm is that the number of mistakes grows only logarithmically with the number of irrelevant attributes in the examples. At the same time, the algorithm is computationally efficient in both time and space. 1.

use of these methods in an online setting suitable for real-time applications. In this paper we consider online learning in a Reproducing Kernel Hilbert Space. By considering classical stochastic gradient descent within a feature space, and the use of some straightforward tricks, we develop simple

(A + n log n) expected time, where A is the number of intersecting pairs reported. The algorithm requires O(n) space in the worst case. Another algorithm computes the convex hull of n points in E d in O(n log n) expected time for d = 3, and O(n bd=2c ) expected time for d ? 3. The algorithm also