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Slides - Biomedical Informatics
Slides - Biomedical Informatics

... The BLAST algorithm.The BLAST algorithm is a heuristic search method that seeks words of length W (default = 3 in blastp) that score at least T when aligned with the query and scored with a substitution matrix. Words in the database that score T or greater are extended in both directions in an attem ...
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Thomas  L. Magnanti and Georgia  Perakis
Thomas L. Magnanti and Georgia Perakis

... we describe in detail in Section 3, stems from some common geometric characteristics that are shared by all these algorithms: they all generate a sequence of "nice" sets of the same type, and use the notion of a center of a "nice" set. At each iteration, the general framework maintains a convex set ...
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Optimal Policies for a Class of Restless Multiarmed

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... 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 ...
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24.1 Rectangular Partitions Question

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Multi-objective optimization

Multi-objective optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, multiattribute optimization or Pareto optimization) is an area of multiple criteria decision making, that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Multi-objective optimization has been applied in many fields of science, including engineering, economics and logistics (see the section on applications for detailed examples) where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives. Minimizing cost while maximizing comfort while buying a car, and maximizing performance whilst minimizing fuel consumption and emission of pollutants of a vehicle are examples of multi-objective optimization problems involving two and three objectives, respectively. In practical problems, there can be more than three objectives.For a nontrivial multi-objective optimization problem, there does not exist a single solution that simultaneously optimizes each objective. In that case, the objective functions are said to be conflicting, and there exists a (possibly infinite) number of Pareto optimal solutions. A solution is called nondominated, Pareto optimal, Pareto efficient or noninferior, if none of the objective functions can be improved in value without degrading some of the other objective values. Without additional subjective preference information, all Pareto optimal solutions are considered equally good (as vectors cannot be ordered completely). Researchers study multi-objective optimization problems from different viewpoints and, thus, there exist different solution philosophies and goals when setting and solving them. The goal may be to find a representative set of Pareto optimal solutions, and/or quantify the trade-offs in satisfying the different objectives, and/or finding a single solution that satisfies the subjective preferences of a human decision maker (DM).
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