Multilevel Coordinate Search (MSC) is an algorithm for bound constrained global optimization using function values only. To do so, the n-dimensional search space is represented by a set of non-intersecting hypercubes (boxes). The boxes are then iteratively split along an axis plane according to the value of the function at a representative point of the box and the box's size.
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- Multilevel Coordinate Search (MSC) is an algorithm for bound constrained global optimization using function values only. To do so, the n-dimensional search space is represented by a set of non-intersecting hypercubes (boxes). The boxes are then iteratively split along an axis plane according to the value of the function at a representative point of the box and the box's size. These two splitting criteria combine to form a global search by splitting large boxes and a local search by splitting areas for which the function value is good. Additionally a local search combining a quadratic interpolant of the function and line searches can be used to augment performance of the algorithm.
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- Multilevel Coordinate Search (MSC) is an algorithm for bound constrained global optimization using function values only. To do so, the n-dimensional search space is represented by a set of non-intersecting hypercubes (boxes). The boxes are then iteratively split along an axis plane according to the value of the function at a representative point of the box and the box's size.
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![[RDF Data]](/statics/sw-cube.png)
