SQG
This paper describes an implementation of a complementary set of methods called stochastic quasi-gradient methods (SQG). These methods are specifically designed having in mind continuous distributions of random parameters and nonlinear optimization problems. They are suited for optimization of simulation models where analytical relations between the objective function and parameters are difficult to trace. Another application area of such methods consists of multiperiod dynamic stochastic models with parametrized decision rules. Supply chain management, financial applications, telecommunications, and energy generation are among the sources of such problems. The most important notion here is the stochastic gradient, which is a statistical estimate of the gradient of the objective function.
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References in zbMATH (referenced in 16 articles , 1 standard article )
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- Adiga, C.; Zaferani, R.K.: Upper bounds for energy of a graph (2008)
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- Nicolai, H.; Peeters, K.: Loop and spin foam quantum gravity: a brief guide for beginners (2007)
- Gaivoronski, Alexei A.: SQG: software for solving stochastic programming problems with stochastic quasi-gradient methods (2005)
- Horata, S.; Egawa, H.S.; Yukawa, T.: Grand-canonical simulation of 4D simplicial quantum gravity (2003)
- Armanious, M.H.; Tadros, S.F.; Dhshan, N.M.: Subdirectly irreducible squags of cardinality $3n$. (2002)
- Sukhatme, Jai; Pierrehumbert, Raymond T.: Surface quasigeostrophic turbulence: the study of an active scalar (2002)
- Zeitler, Herbert: Something old and something new on quasigroups (1998)
- Zeitler, H.: About a special class of quasigroups (1996)
- Zeitler, H.: Steiner systems and Stein quasigroups (1994)
- Uryas’ev, S.P.: Adaptive algorithms of stochastic optimization and game theory. (Adaptivnye algoritmy stokhasticheskoj optimizatsii i teorii igr). (1990)