as formula_153. this phenomenon is often encountered when attempting to approximate a tensor using numerical optimization algorithms. it is sometimes called the problem of "diverging components". it was, in addition, shown that a random low-rank tensor over the reals may not admit a rank-2 approximation with positive probability, leading to the understanding that the ill-posedness problem is an important consideration when employing the tensor rank decomposition.