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An a × b rectangle can be packed with 1 × n strips if and only if n divides a or n divides b. [15] [16] de Bruijn's theorem: A box can be packed with a harmonic brick a × a b × a b c if the box has dimensions a p × a b q × a b c r for some natural numbers p, q, r (i.e., the box is a multiple of the brick.) [15]
Indeed, the ordered simplex is a (closed) fundamental domain for the action of the symmetric group on the n-cube, meaning that the orbit of the ordered simplex under the n! elements of the symmetric group divides the n-cube into ! mostly disjoint simplices (disjoint except for boundaries), showing that this simplex has volume 1/n!.
An example of a non-homogeneous complex is a triangle with a line segment attached to one of its vertices. Pure simplicial complexes can be thought of as triangulations and provide a definition of polytopes. A facet is a maximal simplex, i.e., any simplex in a complex that is not a face of any larger simplex. [2]
An example of simplicial complex, and the corresponding simplex tree data structure. Notice the two lowest nodes have a path of 4 to the node, indicating the 2 3-dimensional simplexes composed of 4 vertices each. In topological data analysis, a simplex tree is a type of trie used to represent efficiently any general simplicial complex.
A key concept in defining simplicial homology is the notion of an orientation of a simplex. By definition, an orientation of a k-simplex is given by an ordering of the vertices, written as (v 0,...,v k), with the rule that two orderings define the same orientation if and only if they differ by an even permutation.
Geometric realization of a 3-dimensional abstract simplicial complex. In combinatorics, an abstract simplicial complex (ASC), often called an abstract complex or just a complex, is a family of sets that is closed under taking subsets, i.e., every subset of a set in the family is also in the family.
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.
If F(x) maps IR^n into IR^(n-1), there is a unique linear interpolant on an (n-1)-dimensional simplex which agrees with the function values at the vertices of the simplex. The second result is: An (n-1)-dimensional simplex can be tested to determine if the unique linear interpolant takes on the value 0 inside the simplex.