Planar Point Location - Brown Computer Science
... To search for a query point, the primary tree is traversed by comparing the y–value of the point with each ∇–node encountered, and the left-to-right ordering of the point with respect to the spanning edge represented by each O–node encountered. If the point is neither to the left nor to the right of ...
... To search for a query point, the primary tree is traversed by comparing the y–value of the point with each ∇–node encountered, and the left-to-right ordering of the point with respect to the spanning edge represented by each O–node encountered. If the point is neither to the left nor to the right of ...
Hidden Markov Models
... to produce continuous probabilities: P(wake), P(deep), and P(REM) Hidden states correspond with recognised sleep stages. 3 continuous probability plots, giving P of each at every second ...
... to produce continuous probabilities: P(wake), P(deep), and P(REM) Hidden states correspond with recognised sleep stages. 3 continuous probability plots, giving P of each at every second ...
Problem Solving Partnerships using the SARA model
... Research shows that a relatively small number of locations and offenders are involved in a relatively large amount of crime; similarly, a small number of victims account for a relatively large amount of victimization. For example, research has found that more than 50% of calls for service in some ar ...
... Research shows that a relatively small number of locations and offenders are involved in a relatively large amount of crime; similarly, a small number of victims account for a relatively large amount of victimization. For example, research has found that more than 50% of calls for service in some ar ...
Theory - NUS School of Computing
... Given a problem, instead of finding a solution, can verify a solution to the problem quickly? ...
... Given a problem, instead of finding a solution, can verify a solution to the problem quickly? ...
Foundations 11 Learning Objectives Logical Reasoning 9 Days
... Explain the relationships among the roots of an equation, the zeros of the corresponding function, and the -intercepts of the graph of the function. Explain, using examples, why the graph of a quadratic function may have zero, one or two -intercepts. Express a quadratic equation in factored fo ...
... Explain the relationships among the roots of an equation, the zeros of the corresponding function, and the -intercepts of the graph of the function. Explain, using examples, why the graph of a quadratic function may have zero, one or two -intercepts. Express a quadratic equation in factored fo ...
CUSTOMER_CODE SMUDE DIVISION_CODE SMUDE
... Explanation of Floyd’s algorithm with an example (6 marks) We can generate the distance matrix with an algorithm that is very similar to Warshall's algorithm. It is called Floyd's algorithm, after its inventor R. Floyd. It is applicable to both undirected and directed 'weighted graphs provided that ...
... Explanation of Floyd’s algorithm with an example (6 marks) We can generate the distance matrix with an algorithm that is very similar to Warshall's algorithm. It is called Floyd's algorithm, after its inventor R. Floyd. It is applicable to both undirected and directed 'weighted graphs provided that ...
Travelling salesman problem
The travelling salesman problem (TSP) asks the following question: Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the origin city? It is an NP-hard problem in combinatorial optimization, important in operations research and theoretical computer science.TSP is a special case of the travelling purchaser problem and the Vehicle routing problem.In the theory of computational complexity, the decision version of the TSP (where, given a length L, the task is to decide whether the graph has any tour shorter than L) belongs to the class of NP-complete problems. Thus, it is possible that the worst-case running time for any algorithm for the TSP increases superpolynomially (perhaps, specifically, exponentially) with the number of cities.The problem was first formulated in 1930 and is one of the most intensively studied problems in optimization. It is used as a benchmark for many optimization methods. Even though the problem is computationally difficult, a large number of heuristics and exact methods are known, so that some instances with tens of thousands of cities can be solved completely and even problems with millions of cities can be approximated within a small fraction of 1%.The TSP has several applications even in its purest formulation, such as planning, logistics, and the manufacture of microchips. Slightly modified, it appears as a sub-problem in many areas, such as DNA sequencing. In these applications, the concept city represents, for example, customers, soldering points, or DNA fragments, and the concept distance represents travelling times or cost, or a similarity measure between DNA fragments. The TSP also appears in astronomy, as astronomers observing many sources will want to minimise the time spent slewing the telescope between the sources. In many applications, additional constraints such as limited resources or time windows may be imposed.