Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Programación Lineal× | Programación no lineal× | |
|---|---|---|
| Campo | Optimización | Optimización |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1947 | 2006 |
| Autor original≠ | George B. Dantzig | Jorge Nocedal & Stephen Wright |
| Tipo≠ | Mathematical programming / continuous optimization | Continuous mathematical optimization |
| Fuente seminal≠ | Dantzig, G.B. (1963). Linear Programming and Extensions. Princeton University Press. ISBN: 9780691059136 | Nocedal, J., & Wright, S. J. (2006). Numerical Optimization (2nd ed.). Springer. ISBN: 978-0-387-30303-1 |
| Alias≠ | LP, linear optimization, Doğrusal Programlama (LP) | NLP optimization, Constrained nonlinear optimization, Smooth optimization, Doğrusal olmayan programlama |
| Relacionados≠ | 4 | 3 |
| Resumen≠ | Linear programming (LP), pioneered by George B. Dantzig in 1947, is a mathematical method for finding the best value of a linear objective function — such as minimum cost or maximum profit — subject to a set of linear inequality and equality constraints. It is the foundational technique in operations research and underlies production planning, resource allocation, logistics, diet problems, and countless other decision-making scenarios across engineering, economics, and the natural sciences. | Nonlinear programming (NLP) is a branch of mathematical optimization concerned with problems in which the objective function or at least one constraint is nonlinear. Formalized comprehensively by Jorge Nocedal and Stephen Wright in their seminal 2006 text, NLP encompasses gradient-based algorithms — including sequential quadratic programming (SQP), interior-point methods, and quasi-Newton approaches — for finding locally or globally optimal solutions to continuous decision problems arising across engineering, economics, and the physical sciences. |
| ScholarGateConjunto de datos ↗ |
|
|