Stefan D. Bruda

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We show that all the problems solvable by a nondeterministic machine with logarithmic work space (NL) can be solved in real time by a parallel machine, no matter how tight the real-time constraints are. We also show that several other real-time problems are in effect solvable in nondeterministic logarithmic space once their real-time constraints are dropped(More)
We investigate the relative computational power of parallel models with shared memory. Based on feasibility considerations present in the literature, we split these models into " lightweight " and " heavyweight, " and then find that the heavyweight class is strictly more powerful than the lightweight class, as expected. On the other hand, we contradict the(More)
Traditionally, interest in parallel computation centered around the speedup provided by parallel algorithms over their sequential counterparts. In this paper, we ask a diierent type of question: Can parallel computers, due to their speed, do more than simply speed up the solution to a problem? We show that for real-time optimization problems, a parallel(More)
A correcting algorithm is one that receives an endless stream of corrections to its initial input data and terminates when all the corrections received have been taken into account. We give a characterization of correcting algorithms based on the theory of data{accumulating algorithms. In particular, it is shown that any correcting algorithm exhibits(More)
In the data{accumulating paradigm, the input is an endless stream. A computation is considered to be nished when all the already received data are processed before another datum arrives. We study sorting algorithms in this paradigm. First, we consider the data arrival law as being polynomial in time. We prove the existence of an upper bound on the running(More)