Parallel Computation
... If there is a parallelization, A, of a sequential algorithm that works in O(f(n)), such that the time complexity of A is g(n), then there is a parallelization, B, of the same algorithm f (n) such that B works in O(g(n)) and uses processors g (n) ...
... If there is a parallelization, A, of a sequential algorithm that works in O(f(n)), such that the time complexity of A is g(n), then there is a parallelization, B, of the same algorithm f (n) such that B works in O(g(n)) and uses processors g (n) ...
Document
... Continuous variables • Do not generally require coding as: – They are already numerical – There is a potentially infinite number of categories ...
... Continuous variables • Do not generally require coding as: – They are already numerical – There is a potentially infinite number of categories ...
L10: k-Means Clustering
... estimate of centers, run on full set (will hopefully be close to converged). • Run a one-pass algorithm (streaming, covered later) getting O(k log k) clusters. Reduce to k clusters at end, but merging extra clusters. Can use another streaming trick where there are a hierarchy of clusters of recent s ...
... estimate of centers, run on full set (will hopefully be close to converged). • Run a one-pass algorithm (streaming, covered later) getting O(k log k) clusters. Reduce to k clusters at end, but merging extra clusters. Can use another streaming trick where there are a hierarchy of clusters of recent s ...
Optimization_2016_JS
... What is optimization? • Optimization = Finding the best way of doing something • The overall problem usually consists of a multitude of decisions that are made simultaneously (decision variables) • The “goodness” of a certain set of decisions is measured by a numerical value called the objective fu ...
... What is optimization? • Optimization = Finding the best way of doing something • The overall problem usually consists of a multitude of decisions that are made simultaneously (decision variables) • The “goodness” of a certain set of decisions is measured by a numerical value called the objective fu ...
Simplex algorithm
In mathematical optimization, Dantzig's simplex algorithm (or simplex method) is a popular algorithm for linear programming. The journal Computing in Science and Engineering listed it as one of the top 10 algorithms of the twentieth century.The name of the algorithm is derived from the concept of a simplex and was suggested by T. S. Motzkin. Simplices are not actually used in the method, but one interpretation of it is that it operates on simplicial cones, and these become proper simplices with an additional constraint. The simplicial cones in question are the corners (i.e., the neighborhoods of the vertices) of a geometric object called a polytope. The shape of this polytope is defined by the constraints applied to the objective function.