H2 Optimal Cooperation of Homogeneous Agents Subject to Delyed
... law reduces information processing by aggregating information from all agents into a single quantity, e.g an average, which is then made available to each of the agents. To the best of our knowledge, the diagonal-plus-rank-one structure first explicitly appeared in (Hovd and Skogestad, 1994), as the ...
... law reduces information processing by aggregating information from all agents into a single quantity, e.g an average, which is then made available to each of the agents. To the best of our knowledge, the diagonal-plus-rank-one structure first explicitly appeared in (Hovd and Skogestad, 1994), as the ...
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... – Builds mathematical and computer models of organizational systems composed of people, machines, and procedures – Uses analytical and numerical techniques to make predictions and decisions based on these models ...
... – Builds mathematical and computer models of organizational systems composed of people, machines, and procedures – Uses analytical and numerical techniques to make predictions and decisions based on these models ...
Efficient robust digital hyperplane fitting with bounded
... d-dimensional cube and some ε > 0, one can build a data structure with O(ε−d ) storage space, in O(N + ε−d logO(1) (ε−1 )) time, such that for a given query hyperplane H, the number of points on and bellow H can be approximately reported in O(1) time, in the following sense: all the points (below H) ...
... d-dimensional cube and some ε > 0, one can build a data structure with O(ε−d ) storage space, in O(N + ε−d logO(1) (ε−1 )) time, such that for a given query hyperplane H, the number of points on and bellow H can be approximately reported in O(1) time, in the following sense: all the points (below H) ...
A Comparative Study of CMA-ES on Large Scale
... is a natural approach for tackling large scale problems. Cooperative Co-evolution (CC) [15] is such a technique that decomposes a large scale problem into a set of sub-problems, each of which is optimised using a separate EA. In the original CC decomposition strategy, each variable is placed in a se ...
... is a natural approach for tackling large scale problems. Cooperative Co-evolution (CC) [15] is such a technique that decomposes a large scale problem into a set of sub-problems, each of which is optimised using a separate EA. In the original CC decomposition strategy, each variable is placed in a se ...
Numerical Solution of Hyperbolic Telegraph Equation Using Method
... Copyright⃝World Academic Press, World Academic Union IJNS.2014.08.15/821 ...
... Copyright⃝World Academic Press, World Academic Union IJNS.2014.08.15/821 ...
Multiple-criteria decision analysis
Multiple-criteria decision-making or multiple-criteria decision analysis (MCDA) is a sub-discipline of operations research that explicitly considers multiple criteria in decision-making environments. Whether in our daily lives or in professional settings, there are typically multiple conflicting criteria that need to be evaluated in making decisions. Cost or price is usually one of the main criteria. Some measure of quality is typically another criterion that is in conflict with the cost. In purchasing a car, cost, comfort, safety, and fuel economy may be some of the main criteria we consider. It is unusual that the cheapest car is the most comfortable and the safest one. In portfolio management, we are interested in getting high returns but at the same time reducing our risks. Again, the stocks that have the potential of bringing high returns typically also carry high risks of losing money. In a service industry, customer satisfaction and the cost of providing service are two conflicting criteria that would be useful to consider.In our daily lives, we usually weigh multiple criteria implicitly and we may be comfortable with the consequences of such decisions that are made based on only intuition. On the other hand, when stakes are high, it is important to properly structure the problem and explicitly evaluate multiple criteria. In making the decision of whether to build a nuclear power plant or not, and where to build it, there are not only very complex issues involving multiple criteria, but there are also multiple parties who are deeply affected from the consequences.Structuring complex problems well and considering multiple criteria explicitly leads to more informed and better decisions. There have been important advances in this field since the start of the modern multiple-criteria decision-making discipline in the early 1960s. A variety of approaches and methods, many implemented by specialized decision-making software, have been developed for their application in an array of disciplines, ranging from politics and business to the environment and energy.