Search results
Results from the WOW.Com Content Network
As the access to materials increased, competition to design the most beautiful patterns rose, with an estimate of over 300 different kogin-zashi patterns being created. In the 20th century, the craft of kogin-zashi was streamlined, establishing the three general types that are seen today: nishi-kogin , higashi-kogin , and mishima-kogin . [ 2 ]
In numerical analysis, hill climbing is a mathematical optimization technique which belongs to the family of local search. It is an iterative algorithm that starts with an arbitrary solution to a problem, then attempts to find a better solution by making an incremental change to the solution.
Conversely, a beam width of 1 corresponds to a hill-climbing algorithm. [3] The beam width bounds the memory required to perform the search. Since a goal state could potentially be pruned, beam search sacrifices completeness (the guarantee that an algorithm will terminate with a solution, if one exists).
Many sashiko patterns were derived from Chinese designs, but just as many were developed by native Japanese embroiderers; for example, the style known as kogin-zashi, which generally consists of diamond-shaped patterns in horizontal rows, is a distinctive variety of sashiko that was developed in Aomori Prefecture.
Stochastic hill climbing is a variant of the basic hill climbing method. While basic hill climbing always chooses the steepest uphill move, "stochastic hill climbing chooses at random from among the uphill moves; the probability of selection can vary with the steepness of the uphill move."
Examples of simplices include a line segment in one-dimensional space, a triangle in two-dimensional space, a tetrahedron in three-dimensional space, and so forth. The method approximates a local optimum of a problem with n variables when the objective function varies smoothly and is unimodal .
Late acceptance hill climbing, created by Yuri Bykov in 2008 [1] is a metaheuristic search method employing local search methods used for mathematical optimization. References [ edit ]
Means–ends analysis [1] (MEA) is a problem solving technique used commonly in artificial intelligence (AI) for limiting search in AI programs.. It is also a technique used at least since the 1950s as a creativity tool, most frequently mentioned in engineering books on design methods.