  We made a comparative analysis of numerical methods for multidimensional
optimization. The main parameter is a number of computations of the test
function to reach necessary accuracy, as it is computationally "slow". For
complex functions, analytic differentiation by many parameters can cause
problems associated with a significant complication of the program and thus
slowing its operation. For comparison, we used the methods: "brute force" (or
minimization on a regular grid), Monte Carlo, steepest descent, conjugate
gradients, Brent's method (golden section search), parabolic interpolation etc.
The Monte-Carlo method was applied to the eclipsing binary system AM Leo.
