Research map / Learning theory and algorithms / Combinatorial and search optimization
Large-Scale Price Optimization via Network Flow
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use proposed network-flow method instead of QPBO | price optimization · Real retail data (1000 prices) | computational time (s): 964[s] -> 36[s] | Helped |
| use proposed method instead of QPBO | price optimization · Real retail data | estimated gross profit: 1245568 -> 1883252 | Helped |
| use proposed price strategy instead of actual historical prices | retail pricing · Real retail data | estimated gross profit: 1403700 -> 1883252 | Helped |
| QPBO strategy compared with actual historical prices | QPBO · Real retail data | estimated gross profit: 1403700 -> 1245568 | Hurt |
| substitute-goods-only (supermodular) regression ranges | proposed, QPBO, QPBOI · Revenue simulation (i) | solution exactness | Helped |
| mixed or complementary goods (non-supermodular) setting | QPBO · Revenue simulation (ii),(iii) | objective value | Hurt |
| use proposed method instead of QPBOI and SDPrelax | price optimization · Revenue simulation (ii),(iii), M=30..300 | computational time | Helped |
| run QPBOI and SDPrelax on 1000-variable real data | QPBOI, SDPrelax · Real retail data | termination | Hurt |
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