Results 251 to 260 of about 256,176 (302)

Stochastic integer programming:General models and algorithms

Annals of Operations Research, 1999
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Klein Haneveld, W.K., van der Vlerk, M.H.   +1 more
openaire   +2 more sources

Two‐stage stochastic integer programming: a survey

Statistica Neerlandica, 1996
Stochastic integer programming is more complicated than stochastic linear programming, as will be explained for the case of the two‐stage stochastic programming model. A survey of the results accomplished in this recent field of research is given.
Schultz, R., Stougie, L., van der Vlerk, M.H.   +2 more
openaire   +3 more sources

Reputation-Aware Federated Learning Client Selection Based on Stochastic Integer Programming

IEEE Transactions on Big Data
Federated Learning(FL) has attracted wide research interest due to its potential in building machine learning models while preserving users’ data privacy.
Xavier Tan, W. Ng, Wei Yang Bryan Lim, Zehui Xiong, Dusist Niyato, Han Yu   +5 more
semanticscholar   +1 more source

On stochastic integer programming

Zeitschrift für Operations Research, 1975
The probability distribution of the optimum (Z) of an integer linear program is discussed in which the elements of the right-hand-side (RHS) are distributed independently. The assumptions of the asymptotic algorithm ofGomory are supposed to hold for each realization of the RHS.
Zimmermann, H.-J., Pollatschek, M. A.
openaire   +2 more sources

Stochastic programming with simple integer recourse

Mathematical Programming, 1993
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Louveaux, François V., van der Vlerk, Maarten H.   +1 more
openaire   +2 more sources

Stochastic Integer Programs

2011
As seen in Section 3.3, properties of stochastic integer programs are scarce. The absence of general efficient methods reflects this difficulty. Several techniques have been proposed in the recent years. As in deterministic integer programs, many of them are based on either a branching scheme or a reformulation scheme. The reader unfamiliar with either
Francois Louveaux, John R. Birge
openaire   +1 more source

Duality Gaps in Stochastic Integer Programming

Journal of Global Optimization, 2000
The note is related to a previous work by \textit{J. R. Birge} and \textit{M. A. H. Dempster} [J. Global Optim. 9, 417-541 (1996; Zbl 0870.90067)] on the duality gap between a mixed integer 0-1 stochastic program and a certain Lagrangian dual. The authors provide an example showing that the duality gap does not necessarily vanish as the size of the ...
Sen, Suvrajeet, Higle, Julia L., Birge, John R.   +2 more
openaire   +2 more sources

Bilevel Integer Programs with Stochastic Right-Hand Sides

INFORMS Journal on Computing, 2021
We develop an exact value function-based approach to solve a class of bilevel integer programs with stochastic right-hand sides. We first study structural properties and design two methods to efficiently construct the value function of a bilevel integer program.
Junlong Zhang, Osman Y. Özaltın
openaire   +1 more source

Stochastic Mixed-Integer Programming

2019
In this chapter we consider a generalization of the recourse model in Chap. 3, obtained by allowing integrality restrictions on some or all of the decision variables. First we give some motivation why such mixed-integer recourse models are useful and interesting. Following the presentation of the general model, we give several examples of applications.
Willem K. Klein Haneveld, Maarten H. van der Vlerk, Ward Romeijnders   +2 more
openaire   +1 more source

Stochastic Integer Programming.

1997
no abstract.
Stougie, L., van der Vlerk, M.H.
openaire   +4 more sources