
L10: k-Means Clustering
... Probably the most famous clustering formulation is k-means. This is the focus today. Note: k-means is not an algorithm, it is a problem formulation. k-Means is in the family of assignment based clustering. Each cluster is represented by a single point, to which all other points in the cluster are “a ...
... Probably the most famous clustering formulation is k-means. This is the focus today. Note: k-means is not an algorithm, it is a problem formulation. k-Means is in the family of assignment based clustering. Each cluster is represented by a single point, to which all other points in the cluster are “a ...
a study on artificial intelligence planning
... Fig.2: O Plan 3-level of agents The constraint based model follows graph plan. Another domain of research works on various methods of exploring state space using ordered binary decisions diagrams.OBDD algorithms are efficient for classical planning but they have a limitation of memory consumption du ...
... Fig.2: O Plan 3-level of agents The constraint based model follows graph plan. Another domain of research works on various methods of exploring state space using ordered binary decisions diagrams.OBDD algorithms are efficient for classical planning but they have a limitation of memory consumption du ...
PTA Program Goal 1 - Fairmont State College
... Emerging - Difficult to follow reasoning, explanation seemed to ramble or no explanation for work, no apparent sense of what audience needed to know, representations did not help clarify your thinking, ...
... Emerging - Difficult to follow reasoning, explanation seemed to ramble or no explanation for work, no apparent sense of what audience needed to know, representations did not help clarify your thinking, ...
GENERAL ASSIGNMENT PROBLEM via Branch and Price
... tasks to n machines such that each task (i=1,2,…,n) is assigned to exactly one machine (j=1,2,…n). ...
... tasks to n machines such that each task (i=1,2,…,n) is assigned to exactly one machine (j=1,2,…n). ...
Optimal Stopping and Free-Boundary Problems Series
... general principle of horizon is also dynamic programming derived. The (the Bellman’s principle). same problems The method of are studied, essential supremum replacing the solves the problem in the Wiener processes case of infinite horizon N by Poisson random ...
... general principle of horizon is also dynamic programming derived. The (the Bellman’s principle). same problems The method of are studied, essential supremum replacing the solves the problem in the Wiener processes case of infinite horizon N by Poisson random ...
slides
... Randomized Quicksort Always output correct answer Takes O(N log N) time on average Likelihood of running O(N log N) time? ...
... Randomized Quicksort Always output correct answer Takes O(N log N) time on average Likelihood of running O(N log N) time? ...
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.