| bibtype |
J -
Journal Article
|
| ARLID |
0376766 |
| utime |
20240903170624.5 |
| mtime |
20120511235959.9 |
| WOS |
000301269800006 |
| title
(primary) (eng) |
Chance constrained problems: penalty reformulation and performance of sample approximation technique |
| specification |
|
| serial |
| ARLID |
cav_un_epca*0297163 |
| ISSN |
0023-5954 |
| title
|
Kybernetika |
| volume_id |
48 |
| volume |
1 (2012) |
| page_num |
105-122 |
| publisher |
| name |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
|
| keyword |
chance constrained problems |
| keyword |
penalty functions |
| keyword |
asymptotic equivalence |
| keyword |
sample approximation technique |
| keyword |
investment problem |
| author
(primary) |
| ARLID |
cav_un_auth*0280972 |
| name1 |
Branda |
| name2 |
Martin |
| full_dept (cz) |
Ekonometrie |
| full_dept (eng) |
Department of Econometrics |
| department (cz) |
E |
| department (eng) |
E |
| institution |
UTIA-B |
| full_dept |
Department of Decision Making Theory |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GBP402/12/G097 |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0281000 |
|
| research |
CEZ:AV0Z10750506 |
| abstract
(eng) |
We explore reformulation of nonlinear stochastic programs with several joint chance constraints by stochastic programs with suitably chosen penalty-type objectives. We show that the two problems are asymptotically equivalent. Simpler cases with one chance constraint and particular penalty functions were studied in [6,11]. The obtained problems with penalties and with a fixed set of feasible solutions are simpler to solve and analyze then the chance constrained programs. We discuss solving both problems using Monte-Carlo simulation techniques for the cases when the set of feasible solution is finite or infinite bounded. The approach is applied to a financial optimization problem with Value at Risk constraint, transaction costs and integer allocations. We compare the ability to generate a feasible solution of the original chance constrained problem using the sample approximations of the chance constraints directly or via sample approximation of the penalty function objective. |
| reportyear |
2013 |
| RIV |
BB |
| num_of_auth |
1 |
| mrcbC52 |
4 O 4o 20231122135039.7 |
| permalink |
http://hdl.handle.net/11104/0209085 |
| mrcbT16-e |
COMPUTERSCIENCECYBERNETICS |
| mrcbT16-f |
0.548 |
| mrcbT16-g |
0.054 |
| mrcbT16-h |
9.6 |
| mrcbT16-i |
0.00164 |
| mrcbT16-j |
0.284 |
| mrcbT16-k |
536 |
| mrcbT16-l |
74 |
| mrcbT16-q |
21 |
| mrcbT16-s |
0.410 |
| mrcbT16-y |
20.28 |
| mrcbT16-x |
0.78 |
| mrcbT16-4 |
Q2 |
| mrcbT16-B |
30.653 |
| mrcbT16-C |
16.667 |
| mrcbT16-D |
Q3 |
| mrcbT16-E |
Q3 |
| arlyear |
2012 |
| mrcbTft |
\nSoubory v repozitáři: 0376766.pdf |
| mrcbU34 |
000301269800006 WOS |
| mrcbU63 |
cav_un_epca*0297163 Kybernetika 0023-5954 Roč. 48 č. 1 2012 105 122 Ústav teorie informace a automatizace AV ČR, v. v. i. |
|