Sentence examples for algorithmic complexities from inspiring English sources

Our study involves an extensive computational and message complexity analysis of each algorithm, and a classification for their deployment characteristics and algorithmic complexities.

BDMPI enables the development of efficient out-of-core parallel distributed memory codes without the high engineering and algorithmic complexities associated with multiple levels of blocking.

The central theme which guides the present development addresses the need to optimize between the algorithmic complexities in advanced front capturing and front tracking methods developed recently for high AIM interfaces with the simplicity requirements imposed by the AMR multi-level dynamic solutions implementation.

We present the algorithms computing the optimal scores and correponding N-maps of a sequence s over a sequence t in Section 2. The algorithmic complexities of these computations are O (| s| × | t| × N) in time and O (| s| + | t| × N) in memory space.

For the subclass of the PUP that belongs to the complexity class P we present a polynomial-time algorithm and give in-depth algorithmic complexity results.

The analysis of algorithmic complexity and numerical stability shows that the algorithm is reliable and can be considered as an efficient alternative to the well-known pencil (state-space) algorithms found in the literature.

The concept of algorithmic complexity defines a sequence by the shortest algorithm needed to generate that sequence.

The feasibility of any pair of insertion positions for pickup and delivery service is checked using a modified version of the scheduling algorithm reported by Hunsaker and Savelsbergh [29] with a reported algorithmic complexity of O (n ).

Applying static pattern detection algorithms from scratch to every window is impractical due to their high algorithmic complexity and the real-time responsiveness required by streaming applications.

This is because the algorithm is not designed to run on such architectures, not because of the algorithmic complexity, but due to the large number of memory accesses required and that are not present in focal-plane processors.

Although HEQ-MA has much larger computational complexity than feature compensation techniques, it can be still regarded as an efficient model adaptation technique compared to other more complex model adaptation techniques such as MLLR due to its predominantly simple algorithmic complexity.