Software for Stochastic Programming
Taken from sci.op-research lp FAQ, question 6.12
Written by Derek Holmes
Your success solving a stochastic program depends greatly on
the characteristics of your problem. The two broad classes of
stochastic programming problems are recourse problems and
chance- constrained (or probabilistically constrained) problems.
Recourse Models
Recourse Problems are staged problems wherein one alteranates
decisions with realizations of stochastic data. The objective is to
minimize total expected costs of all decisions. The main sources of
code (not necessarily public domain) depend on how the data is
distributed and how many stages (decision points) are in the
problem. For discretely distributed multistage problems, a good
package called MSLiP is available from Gus Gassman
(gassmann@ac.dal.ca, written up in Math. Prog. 47,407-423) Also,
for not huge discretely distributed problems, a deterministic
equivalent can be formed which can be solved with a standard
solver. STOPGEN, available via anonymous FTP from this author
is a program which forms deterministic equiv. MPS files from
stopro problems in standard format (Birge, et. al., COAL
newsletter 17). The most recent program for continuously
distributed data is BRAIN, by K. Frauendorfer
(frauendorfer@sgcl1.unisg.ch, written up in detail in the author's
monograph ``Stochastic Two-Stage Programming'', Lecture Notes
in Economics & Math. Systems #392 (Springer-Verlag).
Chance-constrained models
CCP problems are not usually staged, and have a constraint of the
form Pr( Ax <= b ) >= alpha. The solvability of CCP problems
depends on the distribution of the data (A &/v b). I don't know of
any public domain codes for CCP probs., but you can get an idea of
how to approach the problem by reading Chapter 5 by Prof. A.
Prekopa (prekopa@cancer.rutgers.edu) Y. Ermoliev, and R. J-B.
Wets, eds., Numerical Techniques for Stochastic Optimization
(Series in Comp. Math. 10, Springer-Verlag, 1988).
Both Springer Verlag texts mentioned above are good introductory
references to Stochastic Programming. This list of codes is far
from comprehensive, but should serve as a good starting point.
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