Bounding real tensor optimizations via the numerical range

A new method of using the numerical range a matrix to bound optimal value certain optimization problems over real tensor product vectors is presented. This stronger than trivial bounds based on eigenvalues and can be computed significantly faster provided by semidefinite programming relaxations. Numerous applications other hard linear algebra are discussed, such as showing that subspace matrices contains no rank-one matrix, map acting positive.