For an edge , put and . Since , we have .
The random normal has the isotropic Gaussian distribution . Its distribution is invariant under orthogonal transformations; if are linearly independent, its projection onto their plane has a uniformly distributed direction. The signs of its inner products with differ in two sectors of total angle , out of . Thus random hyperplane rounding gives
If , the signs differ with probability one and the same formula holds with . Zero inner products have probability zero, since each is a unit vector.
If the graph has an edge, its corresponding principal block of forces , so the division by used to obtain the Gram matrix is valid. An edgeless graph can instead be colored with one color directly.
A fixed edge is monochromatic exactly when none of the hyperplanes separates its endpoints. Each hyperplane fails to cut it with probability at most , and the hyperplanes are independent. Therefore
Let count monochromatic edges. Applying linearity of expectation to these indicator random variables gives
Independence between different edges is unnecessary; only the independently sampled hyperplanes are used. The signs provide at most colors, whether or not every sign region is nonempty.
If , use a single color. Otherwise choose
The previous bound gives , including the case . By Markov inequality, . Sample the hyperplanes, count the monochromatic edges, and repeat if . The success probability is at least , so at most two draws are needed on average.
For a successful draw, select one endpoint of each monochromatic edge and let be the union of the selected vertices. Then . Every monochromatic edge meets , so the original colors give a proper graph colouring on the induced subgraph with vertex set . Since , the coloring of all vertices is a semicoloring. This is a random alteration method; no additional colors for are required.
The number of available colors satisfies . A simple graph has , hence semicoloring by independent hyperplanes gives

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