Quadratic Functions - Fundamental Coaching Centre
... • Clear any lists within the STAT menu (and if there are graphs in memory, clear them using the Y = menu). Select EDIT from the STAT menu and enter x values under L1 and y values under L2. Both lists should have the same number of entries. • Select Plot 1 on from the STAT PLOT menu and turn the othe ...
... • Clear any lists within the STAT menu (and if there are graphs in memory, clear them using the Y = menu). Select EDIT from the STAT menu and enter x values under L1 and y values under L2. Both lists should have the same number of entries. • Select Plot 1 on from the STAT PLOT menu and turn the othe ...
Longest Common Substring
... common substring problem can be obtained by a bottom-up traversal of the generalized suffix tree T of T (1) , T(2), . . . , T(m) , k-common repeated substring problem. This problem can also be solved in O(m · n) time by a bottom-up traversal of the generalized suffix tree of T (1) , T(2), . . . , T( ...
... common substring problem can be obtained by a bottom-up traversal of the generalized suffix tree T of T (1) , T(2), . . . , T(m) , k-common repeated substring problem. This problem can also be solved in O(m · n) time by a bottom-up traversal of the generalized suffix tree of T (1) , T(2), . . . , T( ...
Sample Average Approximation of Expected Value Constrained
... of solutions of the approximating problem to the true counterpart, and also suggest a simple statistical validation scheme. A number of authors have considered expected value constrained stochastic programs (1). O’Brien [9] studied (1) in the case where the random vector has a finite support. He pro ...
... of solutions of the approximating problem to the true counterpart, and also suggest a simple statistical validation scheme. A number of authors have considered expected value constrained stochastic programs (1). O’Brien [9] studied (1) in the case where the random vector has a finite support. He pro ...
Logical Topology Design
... • All lightpaths are bidirectional: if we set up a lightpath from node i to node j, we also set up a lightpath from node j to node i • Each IP router has at most Δ input ports and Δ output ports – constrains cost of IP routers and number of lightpaths ...
... • All lightpaths are bidirectional: if we set up a lightpath from node i to node j, we also set up a lightpath from node j to node i • Each IP router has at most Δ input ports and Δ output ports – constrains cost of IP routers and number of lightpaths ...
Graphs - Skinners` School Physics
... like 1 square = 3 N or 7 N Choose a scale that will make your graph use most of the sheet of paper. ...
... like 1 square = 3 N or 7 N Choose a scale that will make your graph use most of the sheet of paper. ...
fundamentals of algorithms
... • Build solution to recurrence from bottom up. Write an algorithm that starts with base cases and works its way up to the final solution. Dynamic programming algorithms need to store the results of intermediate sub-problems. This is often but not always done with some kind of table. We will now cove ...
... • Build solution to recurrence from bottom up. Write an algorithm that starts with base cases and works its way up to the final solution. Dynamic programming algorithms need to store the results of intermediate sub-problems. This is often but not always done with some kind of table. We will now cove ...
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.