Research map / Learning theory and algorithms / Submodular and game algorithms
Streaming Algorithm for Monotone k-Submodular Maximization with Cardinality Constraints
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Set C=0.5D (and D=B(2^{1/B}-1)) in the streaming primal-dual algorithm for k-submodular maximization | PrimalDual (Algorithm 1) · Influence maximization (Facebook) and sensor placement (Intel Lab), k=3 | objective value vs Greedy | No clear effect |
| Use stochastic Greedy instead of deterministic lazy Greedy as baseline | Offline Greedy baseline · Influence maximization and sensor placement | function value and number of evaluations | Hurt |
| Use the streaming primal-dual algorithm with its approximation guarantee rather than offline Greedy (1/3) | monotone k-submodular maximization · Theory | approximation guarantee: guarantee 0.2953 as B→∞ vs 1/3 for offline Greedy | Hurt |
| Increase the minimum budget B (larger cardinality constraints) | PrimalDual (Algorithm 1) · Theory | approximation guarantee: from 1/4 at B=1 to approx 0.2953 as B→∞ | Helped |
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