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Stochastic programming

In the field of mathematical optimization, stochastic programming is a framework for modeling optimization problems that involve uncertainty. A stochastic program is an optimization problem in which some or all problem parameters are uncertain, but follow known probability distributions. This framework contrasts with deterministic optimization, in which…

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Stochastic programming

Nodes49
Edges48
Triples107
Avg. degree1.96
Density0.040816
Components1

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Stochastic programming

Top relations

related to Further reading · 63
Stochastic programming → Alan, Alexander, Alexander Shapiro, Andrzej, Andrzej Ruszczynski, András Prékopa, Applications, Applied Mathematics, Archived, Birge, Ch, Chichester, Darinka, Dentcheva, Dordrecht, Elsevier, Financial Engineering, François, Handbooks, Industrial
related to Example: multistage portfolio optimization · 11
Stochastic programming → At, Consider, It, Let, Similarly, Suppose, T-1, The, This, Thus, We
related to Modelling languages · 11
Stochastic programming → AIMMS, All, AMPL, An, Expected, Extended Mathematical Programming, Extensions, GAMS, SAMPL, SP, Value
related to Statistical inference · 4
Stochastic programming → Consider, Here, In, Xi
has method · 2
Stochastic programming → Scenario-based, Several
related to External links · 2
Stochastic programming → Page, Stochastic Programming Community Home
related to Two-stage problem definition · 2
Stochastic programming → Ax, The
see also · 2
Stochastic programming → Chance-constrained, Stochastic ProgrammingEntropic
is a · 1
Stochastic programming → framework for modeling optimization problems that involve uncertainty

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Important terminology

displaystyle problem stochastic xi programming optimization random dots optimal scenarios sample probability scenario value one decision two-stage deterministic linear equivalent

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
Stochastic programmingis aframework for modeling optimization problems that involve uncertainty0.90text
CPLEXinstance ofOptimizers0.80text
and GLPK can solve large linear/nonlinear problemsinstance ofOptimizers0.80text
fledging in birdsinstance oflife-history transitions0.80text
egg laying in parasitoid wasps have shown the value of this modelling technique in explaining the evolution of behavioural decision makinginstance oflife-history transitions0.80text
weatherinstance ofoften it is used by resource economists to analyze bioeconomic problems where the uncertainty enters in0.80text
etc.Exampleinstance ofoften it is used by resource economists to analyze bioeconomic problems where the uncertainty enters in0.80text
etcinstance ofoften it is used by resource economists to analyze bioeconomic problems where the uncertainty enters in0.80text
Value at riskinstance ofchance constraints and risk measures0.80text
Expected shortfallinstance ofchance constraints and risk measures0.80text
Stochastic programminghas methodSeveral0.60section
Stochastic programminghas methodScenario-based0.60section

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