Сравнение методов

	Кривая обучения (степенной закон практики)	Нелинейное программирование
Область	Образовательная аналитика	Оптимизация
Семейство	Regression model	Process / pipeline
Год появления	1936	2006
Автор метода	Theodore Wright	Jorge Nocedal & Stephen Wright
Тип	Power-law regression model	Continuous mathematical optimization
Основополагающий источник	Wright, T. P. (1936). Factors affecting the cost of airplanes. Journal of the Aeronautical Sciences, 3(4), 122–128.	Nocedal, J., & Wright, S. J. (2006). Numerical Optimization (2nd ed.). Springer. ISBN: 978-0-387-30303-1
Другие названия	Power Law of Practice, Experience Curve, Wright's Law, Öğrenme Eğrisi	NLP optimization, Constrained nonlinear optimization, Smooth optimization, Doğrusal olmayan programlama
Связанные	3	3
Сводка	The learning curve models how performance improves predictably as cumulative experience accumulates. Formalized by Theodore Wright in 1936 using aircraft manufacturing data, it expresses the relationship between the number of practice trials (or production units) and the time or cost per unit as a power-law function. It is widely applied in educational psychology, industrial engineering, health professions training, and human factors research whenever repeated task execution is the mechanism of skill acquisition.	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.