Effective business practices
Kurt Marti has written: 'Stochastic Optimization' 'Coping with uncertainty' -- subject(s): Mathematical models, Uncertainty 'Computation of efficient solutions of discretely distributed stochastic optimization problems' 'Descent directions and efficient solutions in discretely distributed stochastic programs' -- subject(s): Stochastic processes, Mathematical optimization
Zelda B. Zabinsky has written: 'Stochastic adaptive search for global optimization' -- subject(s): Mathematical optimization, Search theory, Stochastic processes
Daniel Kuhn has written: 'Generalized bounds for convex multistage stochastic programs' -- subject(s): Mathematical optimization, Stochastic approximation
Tuula Hakala has written: 'A stochastic optimization model for multi-currency bond portfolio management' -- subject(s): Mathematical models, Interest rates, Risk, Stochastic programming
Effective business practices
Kenneth Lange has written: 'Applied probability' -- subject(s): Probabilities, Stochastic processes 'Optimization' 'Numerical analysis for statisticians' -- subject(s): Numerical analysis, Mathematical statistics 'Applied probability' -- subject(s): Probabilities, Stochastic processes
We describe basic ideas of the stochastic quantization which was originally proposed by Parisi and Wu. We start from a brief survey of stochastic-dynamical approaches to quantum mechanics, as a historical background, in which one can observe important characteristics of the Parisi-Wu stochastic quantization method that are different from others. Next we give an outline of the stochastic quantization, in which a neutral scalar field is quantized as a simple example. We show that this method enables us to quantize gauge fields without resorting to the conventional gauge-fixing procedure and the Faddeev-Popov trick. Furthermore, we introduce a generalized (kerneled) Langevin equation to extend the mathematical formulation of the stochastic quantization: It is illustrative application is given by a quantization of dynamical systems with bottomless actions. Finally, we develop a general formulation of stochastic quantization within the framework of a (4 + 1)-dimensional field theory.
Stochastic Models was created in 1985.
Dietrich Stoyan has written: 'Mathematische Methoden in der Operationsforschung' -- subject(s): Mathematical optimization, Materials handling 'Stochastische Geometrie' -- subject(s): Stochastic geometry
Point method refers a class of algorithms aimed at solving linear and nonlinear convex optimization problems
G. Adomian has written: 'Stochastic systems' -- subject(s): Stochastic differential equations, Stochastic systems
The DGKC method, also known as the dual gradient descent with conjugate curvature method, is an optimization algorithm used to solve nonlinear programming problems. It combines the conjugate gradient method with the idea of dual ascent for achieving faster convergence rates. This method is particularly useful for large-scale optimization problems with nonlinear constraints.