Stochastic Programming - Index of

Index
absolutely continuous, 31
accumulated return function, see dynamic
programming
almost everywhere (a.e.), 27
almost surely (a.s.), 16, 28
approximate optimization, 218
augmented Lagrangian, see Lagrange
function
backward recursion, see dynamic programming
barrier function, 97
basic solution, see feasible
basic variables, 56, 65
Bellman, 110, 115–117, 121
optimality principle, 115
solution procedure, 121
Benders’ decomposition, 213, 233
block-separable recourse, 233
bounds
Edmundson–Madansky upper bound,
181–185, 192, 194, 203, 234
Jensen lower bound, 179–182, 184,
185, 218, 220, 233
limited information, 234
piecewise linear upper bound, 185–
190, 234
example, 187–189
stopping criterion, 212
bunching, 230
cell, 183, 190, 196, 201, 203, 212, 234
chance constraints, see stochastic program
with
chance node, see decision tree
complementarity conditions, 84, 89
1 Italic page numbers (e.g. 531) indicate to
literature.
complete recourse, see stochastic program
with
cone, see convex
connected network, see networks
convex
cone, 60
polar, 163
polyhedral cone, 39, 60, 69, 160
generating elements, 60, 69, 79,
163, 166
polyhedral set, 62
polyhedron, 58, 91, 234
vertex, 58, 175
convex hull, 43, 57
convex linear combination, 57
cross out, see decision tree
cut, see networks
cutting plane method, see methods
(nlp)
decision node, see decision tree
decision tree
chance node, 124
cross out, 126
decision node, 124
deterministic, 121–123
definition, 121
folding back, 123
stochastic, 124–129
density function, 30, 51
descent direction, 84, 226
deterministic equivalent, 21, 31–36, 103
deterministic method, 217
distribution function, 30
dual decomposition
data structure, 17, 42
master program, 173
method, 75–80, 161, 168, 173
dual program, see linear program
duality gap, 74