This article provides a comparative analysis of the symbolic and numerical computing capabilities of MathCad and Python programming environments for solving engineering optimization problems. Special attention is paid to linear programming problems, and the simplex method and graphical solution methods are considered in detail. The graphical representation of the solution domain, the value of the objective function at the corner points, and the simplex path are shown. The results of the study show that the Python library ecosystem has an advantage in solving a wide range of engineering problems, and MathCad is a convenient environment for documentation and training.
| Mualliflar | Sa'dullayeva, Maftuna |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2026-06-10 |
| Son | 2 |
| Betlar | 364-367 |
| Til | Rus |
chiziqli dasturlash, simpleks metodi, yechimlar sohasi, MathCad, Python, scipy.optimize, optimallashtirish, muhandislik, linear programming, simplex method, solution domain, MathCad, Python, scipy.optimize, optimization, engineering
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