Research map / Bandits and online learning / Online convex optimization and games
Nearly Optimal Competitive Ratio for Online Allocation Problems with Two-sided Resource Constraints and Finite Requests
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a threshold price vector initialized from an estimate and dynamically updated at each step (Algorithm 4, known ξ*) | Two-sided constrained online allocation · Stochastic i.i.d. requests with lower and upper resource bounds | competitive ratio (1 − O(ε/(ξ*−ε))) of W0 w.h.p. | Helped |
| Estimate unknown feasibility measure ξ* from an initial stage of requests (Algorithm 6) instead of assuming it known | Two-sided constrained online allocation · Stochastic i.i.d. requests, ξ* unknown | competitive ratio order | Mixed |
| Lower-bound deviation: enlarging lower bounds by τT ā_k (Algorithm with known distribution) to ensure feasibility (ablation) | Algorithm P̃ (known distribution) · Stochastic i.i.d. requests | objective value and constraint satisfaction w.p. 1−ε | Helped |
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