Examples of hypercube

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This linear mapping transforms a hypercube into a polytope.
One thousand random simulations, using a latin hypercube sampling technique, were run.
The decoherence operators in equation (85) project the walker onto one of the vertices of the hypercube chosen uniformly at random.
Although the answer to this question follows from our work on the hypercube below, we include a direct treatment here.
Tensorization techniques give the right order for (and even, exactly the right constant) in the case of the hypercube.
For example, in a small diameter network such as a hypercube, processes might be transferred only to adjacent processors.
We begin with a theorem which gives bounds on the critical probability for majority bootstrap percolation on the hypercube.
The hypercube contains 16 vertices, 24 faces, and 8 cubes.
Conversely, assume that the lattice () is a hypercube, that is, () is isomorphic to 1 with 1 the rank 1 chain.
If all the torque limits were equal, the m-dimensional parallelepiped would result in a hypercube.
It follows that a choice with r bad sets lies at distance r in the hypercube from a choice with no bad sets (a good choice, we will say).
Latin hypercube sampling is a sampling technique characterized by stratified sampling without replacement.
Thus, the 2n clumps correspond precisely to the 2n vertices of the hypercubes, so any isometry of the hypercube is an isometry of the truncated permutahedron, and vice versa.
Recall equation (41) showing that the adjacency matrix for the hypercube decomposes into a sum of tensor products, with each acting only on a single qubit.
In a subsequent paper [9] we shall investigate 2-neighbour bootstrap percolation on high-dimensional lattices, and show that the problem essentially reduces to the equivalent question for the hypercube.
With the addition of one degree of redundancy, one of the facets of the hypercube is described by one actuator at an extreme capability and three torques in transition.
Kempe (2003b; 2005) proved that a quantum walk can travel from one corner of a hypercube to the opposite corner exponentially faster than a classical random walk.

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