  Highly concentrated functions play an important role in many research fields
including control system analysis and physics, and they turned out to be the
key idea behind inverse Laplace transform methods as well.
  This paper uses the matrix-exponential family of functions to create highly
concentrated functions, whose squared coefficient of variation (SCV) is very
low. In the field of stochastic modeling, matrix-exponential functions have
been used for decades. They have many advantages: they are easy to manipulate,
always non-negative, and integrals involving matrix-exponential functions often
have closed-form solutions. For the time being there is no symbolic
construction available to obtain the most concentrated matrix-exponential
functions, and the numerical optimization-based approach has many pitfalls,
too.
  In this paper, we present a numerical optimization-based procedure to
construct highly concentrated matrix-exponential functions. To make the
objective function explicit and easy to evaluate we introduce and use a new
representation called hyper-trigonometric representation. This representation
makes it possible to achieve very low SCV.
