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Query plans for nested SQL queries can also be chosen using the same dynamic programming algorithm as used for join ordering, but this can lead to an enormous escalation in query optimization time. So some database management systems use an alternative rule-based approach that uses a query graph model.
Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute 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.
In information retrieval, Okapi BM25 (BM is an abbreviation of best matching) is a ranking function used by search engines to estimate the relevance of documents to a given search query. It is based on the probabilistic retrieval framework developed in the 1970s and 1980s by Stephen E. Robertson , Karen Spärck Jones , and others.
Some query tools can generate embedded hints in the query, for use by the optimizer. Some databases - like Oracle - provide a plan table for query tuning. This plan table will return the cost and time for executing a query. Oracle offers two optimization approaches: CBO or Cost Based Optimization; RBO or Rule Based Optimization
In terms of mathematical optimization, dynamic programming usually refers to simplifying a decision by breaking it down into a sequence of decision steps over time. This is done by defining a sequence of value functions V 1 , V 2 , ..., V n taking y as an argument representing the state of the system at times i from 1 to n .
The basic algorithm – greedy search – works as follows: search starts from an enter-point vertex by computing the distances from the query q to each vertex of its neighborhood {: (,)}, and then finds a vertex with the minimal distance value. If the distance value between the query and the selected vertex is smaller than the one between the ...
Specific applications of search algorithms include: Problems in combinatorial optimization, such as: . The vehicle routing problem, a form of shortest path problem; The knapsack problem: Given a set of items, each with a weight and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as ...
Sequential minimal optimization; Sequential quadratic programming; Simplex algorithm; Simulated annealing; Simultaneous perturbation stochastic approximation; Social cognitive optimization; Space allocation problem; Space mapping; Special ordered set; Spiral optimization algorithm; Stochastic dynamic programming; Stochastic gradient Langevin ...