How would you go about testing all possible combinations of additions from a given set N of numbers so they add up to a given final number? A brief example: Set of numbers to add: N = {1,5,22,15,0...
A common algorithm with O (log n) time complexity is Binary Search whose recursive relation is T (n/2) + O (1) i.e. at every subsequent level of the tree you divide problem into half and do constant amount of additional work.
Robust peak detection algorithm (using z-scores) I came up with an algorithm that works very well for these types of datasets. It is based on the principle of dispersion: if a new datapoint is a given x number of standard deviations away from a moving mean, the algorithm gives a signal. The algorithm is very robust because it constructs a separate moving mean and deviation, such that previous ...
Algorithm A can't tell the difference between two similar inputs instances where only x 's value changes. If x is the minimum in one of these instances and not in the other, then A will fail to find the minimum on (at least) one of these two instances. In other words, finding the minimum in an array is in not in o(n) and is therefore in 𝛺(n).
Both choices refer to what algorithm the identity provider uses to sign the JWT. Signing is a cryptographic operation that generates a "signature" (part of the JWT) that the recipient of the token can validate to ensure that the token has not been tampered with. RS256 (RSA Signature with SHA-256) is an asymmetric algorithm, and it uses a public/private key pair: the identity provider has a ...
1 Here's an algorithm faster than everybody else's algorithm for most cases. It's new and elegant. We spend O(n * log(n)) time building a table that will allow us to test point-in-polygon in O(log(n) + k) time. Rather than ray-tracing or angles, you can get significantly faster results for multiple checks of the same polygon using a scanbeam table.
While solving a geometry problem, I came across an approach called Sliding Window Algorithm. Couldn't really find any study material/details on it. What is the algorithm about?
O (n) means that the algorithm's maximum running time is proportional to the input size. basically, O (something) is an upper bound on the algorithm's number of instructions (atomic ones). therefore, O (logn) is tighter than O (n) and is also better in terms of algorithms analysis.
This is a simple question from algorithms theory. The difference between them is that in one case you count number of nodes and in other number of edges on the shortest path between root and concrete
The peak-finding algorithm would find the location of these peaks (not just their values), and ideally would find the true inter-sample peak, not just the index with maximum value, probably using quadratic interpolation or something.
