China
Africa
Explore chapters and articles related to this topic
Polynomial Time Algorithms
Published in Craig A. Tovey, Linear Optimization and Duality, 2020
Searching depends on the data structure used to hold the data set. One can often search an ordered set of n values in O(logn) time with a binary search algorithm. Binary search maintains two locations, v+ and v-, which are strict upper and lower bounds, respectively, on the location of the value being searched for. Check the value at location v=12(v++v−). If the desired value is not at location v, then v is either too high or too low. Update the upper or lower bound and repeat. This algorithm requires that the set be ordered but not that the value being searched for be present. Question: Why not?7 If the value is not present, binary search tells you where it would fit into the ordered set.
A Unified Method for Calculating Parasitic Capacitive and Resistive Coupling in VLSI Circuits
Published in Thomas Noulis, Noise Coupling in System-on-Chip, 2018
Alkis A. Hatzopoulos, Michael G. Dimopoulos
Run time can be further improved by applying typical optimization techniques to phases 2 and 3. For example, any common optimized search algorithm technique, such as the binary search, can be used for step 4 of both phase 2 and phase 3. Binary search for example has a complexity of O(log2n), which is low compared to the O(n) complexity of the exhaustive search.
Design for Optimisation
Published in Keith L. Richards, The Engineering Design Primer, 2020
In computer science, binary search, also known as half-interval search, logarithmic search or binary chop, is a search algorithm that finds the position of a target value within a sorted array. Binary search compares the target value to the middle element of the array; if they are unequal, the half in which the target cannot lie is eliminated and the search continues on the remaining half until the target value is found. If the search ends with the remaining half being empty, the target is not in the array.
Modifications of the Robertson Method for Calculating Correlated Color Temperature to Improve Accuracy and Speed
Published in LEUKOS, 2023
Doug Baxter, Michael Royer, Kevin Smet
There are several ways to search a LUT that will all return the correct answer, but the number of calculations needed to reach the solution and type of operations being performed vary, which can affect the time needed to complete the calculation. Important search methods that may be used include: Linear: A linear search means the distance between the chromaticity of the test light source and the chromaticity coordinates in the LUT is calculated for each row of the LUT in sequential order until the point where the distance begins to increase (one row after the minimum). A linear search is simple to program and is used, for example, in Excel tools supplied with ANSI/IES TM-30 (IES 2020) and CIE 224 (CIE 2017)Binary (or bisection search): A binary search progresses by repeatedly dividing the array in half until the minimum distance is found. The first, last, and middle distance are first calculated, which allows identification of the portion of the array in which the minimum falls, producing the next interval to be searched. Executing a binary search provides an overall time savings compared to a linear search. Some implementations of CCT calculations, such as those in LuxPy (Smet 2020), implement a binary search.Golden section: A golden section search algorithm also divides the array into parts, but unlike the binary method, they are unequal. The method maintains four points, with the three intervals having the golden ratio. As with a binary search, the minimum is found to be iteratively reducing the region being searched. Zhang (2019) documented the use of a golden section search, in combination with a technique of iteratively creating new LUTs within a specified region, to estimate CCT at an accuracy determined by a stopping criterion.Fibonacci: The Fibonacci search algorithm is closely related to the golden section search. The relative size of the two parts of the searched array is the same as consecutive Fibonacci numbers – this ratio approaches the golden ratio. The size of the subintervals is not perfectly constant, however.