Improved Time Complexities of Algorithms for the Directional Minimum Energy Broadcast ProblemAbility to find a low-energy broadcast routing quickly is vital to a wireless system's energy efficiency. Directional
In general, you can determine the time complexity by analyzing the program’s statements (go line by line). However, you have to be mindful how are the statements arranged. Suppose they are inside a loop or have function calls or even recursion. All these factors affect the runtime of you...
Total number of times count++ will run is N+N/2+N/4+...+1=2∗N. So the time complexity will be O(N).The table below is to help you understand the growth of several common time complexities, and thus help you judge if your algorithm is fast enough to get an Accepted ( ...
These type of algorithms never have to go through all of the input, since they usually work by discarding large chunks of unexamined input with each step. This time complexity is generally associated with algorithms that divide problems in half every time, which is a concept known as “Divide ...
Existing algorithms for learning Boolean networks (BNs) have time complexities of at least O(N . n(0.7(k+1))), where n is the number of variables, N is the number of samples and k is the number of inputs in Boolean functions. Some recent studies propose more efficient methods with O...
4 are of particular importance when determining upper bounds on the complexities of SAT(⋅) problems, since T(S)≤T(S′) if 〈S〉∄⊆〈S′〉∄. With the help of the results from Section 5 we can in fact get tight bounds on the complexity for all languages below Γ1/3. ...
This diagram illustrates the dependencies and time complexities that exist between individual measures and their computation for all nodes in a network. It was derived from [27]. n is the number of nodes. Every measure is calculated from its predecessors. For example, the transitivity (right ...
Most Bayesian (or semi-Bayesian) inference algorithms have either \({\mathscr {O}}(mb^2)\) or \({\mathscr {O}}(m)\) time complexities, where b is the number of communities. Notably, a non-parametric Bayesian approach achieves \({\mathscr {O}}(m)\) without explicitly dependent on ...
One of the most important aspects of time series is their degree of stochasticity vs. chaoticity. Since the discovery of chaotic maps, many algorithms have been proposed to discriminate between these two alternatives and assess their prevalence in real-world time series. Approaches based on the ...
In addition, the time complexities of the learning processes and the accuracies of results are greatly determined by the optimization process for all types of the deep network models [10]. So it is a key issue to design a algorithm for non-stationary RNN model. The most basic and widely ...