Compară metode

	Modelul Newsvendor	Optimizare Stocastică
Domeniu	Cercetare operațională	Optimizare
Familie	Regression model	Process / pipeline
Anul apariției	1951	1951 (SGD); 2014 (Adam)
Autorul original	Arrow, Harris & Marschak	—
Tip	Stochastic single-period inventory optimization	Gradient-based iterative optimization
Sursa seminală	Arrow, K. J., Harris, T., & Marschak, J. (1951). Optimal inventory policy. Econometrica, 19(3), 250–272.	Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. Annals of Mathematical Statistics, 22(3), 400-407.
Denumiri alternative	Newsboy Model, Single-Period Inventory Model, Christmas Tree Problem, Gazete Satıcısı Modeli	Stokastik Optimizasyon (SGD & Varyantları), stochastic gradient descent, SGD, Adam
Înrudite	3	3
Rezumat	The Newsvendor Model is a single-period stochastic inventory optimization framework that determines the profit-maximizing order quantity when demand is uncertain and unsold units cannot be carried forward. Formally introduced by Arrow, Harris, and Marschak (1951) in their foundational work on optimal inventory policy, the model balances the cost of ordering too much (overage) against the cost of ordering too little (underage) to yield a closed-form optimality condition known as the critical ratio.	Stochastic optimization is a family of iterative methods that minimize an objective function by computing gradients on randomly sampled subsets of data — mini-batches — rather than on the entire dataset at once. Pioneered by Robbins and Monro in 1951 as stochastic approximation, the approach became the standard engine for training large-scale machine-learning models through variants such as SGD with momentum, AdaGrad, RMSProp, and Adam.