Constructing university timetable using constraint
... involves three variables then it is known as path consistency. In general a graph is k-consistent if there exists (k-1) variables that satisfy all the constraints among these variables and there also exists a value for this kth variable that satisfies all the constraints among these k variables [25] ...
... involves three variables then it is known as path consistency. In general a graph is k-consistent if there exists (k-1) variables that satisfy all the constraints among these variables and there also exists a value for this kth variable that satisfies all the constraints among these k variables [25] ...
Slide 1
... do not perform satisfactorily when they are used to obtain multiple solutions, since it is not guaranteed that different solutions will be obtained even with different starting points in multiple runs of the algorithm. Evolutionary Algorithms are however a very popular approach to obtain multiple so ...
... do not perform satisfactorily when they are used to obtain multiple solutions, since it is not guaranteed that different solutions will be obtained even with different starting points in multiple runs of the algorithm. Evolutionary Algorithms are however a very popular approach to obtain multiple so ...
High–performance graph algorithms from parallel sparse matrices
... SGI Altix with 128 Itanium II processors with 128G RAM (total, non-uniform memory access). We used a graph generated with scale 21. This graph has 2, 097, 152 vertices. The multigraph has 320, 935, 185 directed edges, whereas the undirected graph corresponding to the multigraph has 89, 145, 367 edge ...
... SGI Altix with 128 Itanium II processors with 128G RAM (total, non-uniform memory access). We used a graph generated with scale 21. This graph has 2, 097, 152 vertices. The multigraph has 320, 935, 185 directed edges, whereas the undirected graph corresponding to the multigraph has 89, 145, 367 edge ...
Solutions
... Solution outline. Every path of the frog has to go through the square in the middle. On its way from this square the frog decides four times whether to jump directly up or diagonally, thus the number of ways from the middle up is 24 = 16. The number of ways from the bottom row to the middle is the s ...
... Solution outline. Every path of the frog has to go through the square in the middle. On its way from this square the frog decides four times whether to jump directly up or diagonally, thus the number of ways from the middle up is 24 = 16. The number of ways from the bottom row to the middle is the s ...
... The LR which is one of the most successful approaches for UC is dual optimization technique. This method obtains an appropriate condition to generate feasible solution for UC. One of the most obvious advantages of the LR method is its quantitative measure of the solution quality since the cost of th ...
Construct and justify arguments and solve multistep problems
... Find the measures of each of the sides in Triangle JKL. The perimeter of Triangle JKL is 164 cm. Show your work to solve the problem and give an explanation for the rationale behind setting up the problem. The measure of'is x cm. — The measure of^ls 4 cm less than 8 times the measure oftfjc^ is 3 cm ...
... Find the measures of each of the sides in Triangle JKL. The perimeter of Triangle JKL is 164 cm. Show your work to solve the problem and give an explanation for the rationale behind setting up the problem. The measure of'is x cm. — The measure of^ls 4 cm less than 8 times the measure oftfjc^ is 3 cm ...
Quantile Regression for Large-scale Applications
... We use k · k1 to denote the element-wise `1 norm for both vectors and matrices; and we use [n] to denote the set {1, 2, . . . , n}. For any matrix A, A(i) and A(j) denote the i-th row and the j-th column of A, respectively; and A denotes the column space of A. For simplicity, we assume A has full co ...
... We use k · k1 to denote the element-wise `1 norm for both vectors and matrices; and we use [n] to denote the set {1, 2, . . . , n}. For any matrix A, A(i) and A(j) denote the i-th row and the j-th column of A, respectively; and A denotes the column space of A. For simplicity, we assume A has full co ...
Adapted Dynamic Program to Find Shortest Path in a Network
... (1984) provided a taxonomy and annotation for the shortest path algorithms. However, due to failure, maintenance or other reasons, we encountered different kinds of uncertainties in practice, and these uncertainties must be taken into account. For example, the lengths of the arcs are assumed to repr ...
... (1984) provided a taxonomy and annotation for the shortest path algorithms. However, due to failure, maintenance or other reasons, we encountered different kinds of uncertainties in practice, and these uncertainties must be taken into account. For example, the lengths of the arcs are assumed to repr ...
PPT - CS
... – There is always at least one process that can advance: • If a process is ahead of all others it can advance • If no process is ahead of all others, then there is more than one process at the top stage, and one of them can advance. ...
... – There is always at least one process that can advance: • If a process is ahead of all others it can advance • If no process is ahead of all others, then there is more than one process at the top stage, and one of them can advance. ...
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.