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Geometry of Online Packing Linear Programs Marco Molinaro and R. Ravi Carnegie Mellon University Packing Integer Programs (PIPs) β’ Non-negative c, A, b β’ Max ππ₯ n st A π₯ β {0,1}π β’ A has entries in [0,1] x β€ b m Online Packing Integer Programs β’ β’ β’ β’ Adversary chooses values for c, A, b β¦but columns are presented in random order β¦when column comes, set variable to 0/1 irrevocably b and n are known upfront A c A n 1 0 x β€ b Online Packing Integer Programs β’ Goal: Find feasible solution that maximizes expected value β’ 1 β π -competitive: E ALG β₯ 1 β π OfflineIP Previous Results β’ First online problem: secretary problem [Dynkin 63] β’ B-secretary problem (m=1, b=B, A is all 1βs) [Kleinberg 05] (1 β π)-competitive for π΅ = Ξ© do not depend on n 1 π2 β’ PIPs (B=min bi) [FHKMS 10, AWY] (1 β π)-competitive for π΅ = Ξ© need π΅ = Ξ© depends on n π log 2 π 1 log 2 π π π Main Question and Result β’ Q: Do general PIPs become more difficult for larger n? β’ A: No! Main result Algorithm 1 β π -competitive when π΅ = Ξ© π2 log π2 π High-level Idea 1. Online PIP as learning 2. Improving learning error using tailored covering bounds 3. Geometry of PIPs that allow good covering bounds 4. Reduce general PIP to above For this talk: β’ π=1 β’ Every right-hand side ππ = π΅ β’ Show weaker bound π΅ = Ξ© π2 log π3 π Online PIP as Learning 1) Reduction to learning a classifier [DH 09] Linear classifier: given (dual) vector π β π π , π₯(π)π‘ = 1 iff ππ΄π‘ β 1 < 0 π¨π 0 0 1 1 1 0 π(π)π 1 π Online PIP as Learning 1) Reduction to learning a classifier [DH 09] Linear classifier: given (dual) vector π β π π , π₯(π)π‘ = 1 iff ππ΄π‘ β 1 < 0 Claim: If the classification π₯(π) given by π satisfies 1) [Feasible] π‘ π΄π‘π π₯(π)π‘ β€π΅ 2) [Packs tightly] If ππ > 0, then π‘ π΄π‘π π₯(π)π‘ β₯ 1βπ π΅ then π₯(π) is (1βπ) optimal. Moreover, such classification always exists. Online PIP as Learning 1) Reduction to learning [DH 09] Linear classifier: given (dual) vector π β π π , set π₯(π)π‘ = 1 iff ππ΄π‘ β 1 < 0 Claim: If the classification π₯(π) given by π satisfies 1) [Feasible] π‘ π΄π‘π π₯(π)π‘ β€π΅ 2) [Packs tightly] If ππ > 0, then π‘ π΄π‘π π₯(π)π‘ β₯ 1βπ π΅ then π₯(π) is (1βπ) optimal. Moreover, such classification always exists. Online PIP as Learning 2) Solving PIP via learning a) See first π fraction of columns b) Compute appropriate π for sampled IP [ππ > 0], π‘β€ππ π΄π‘π π₯(π)π‘ β€ ππ΅ βπ 2 π΅ π‘β€ππ π΄π‘π π₯(π)π‘ β₯ 1 β π ππ΅ +π 2 π΅ c) Use π to classify remaining columns Online PIP as Learning 2) Solving PIP via learning π a) See first π fraction of columns b) Compute appropriate π for sampled IP [ππ > 0], π‘β€ππ π΄π‘π π₯(π)π‘ β€ ππ΅ βπ 2 π΅ π‘β€ππ π΄π‘π π₯(π)π‘ β₯ 1 β π ππ΅ +π 2 π΅ c) Use π to classify remaining columns Probability of learning good classifier: β’ Consider a classifier π that overfills some budget: π‘ π΄π‘π π₯(π)π‘ > π΅ β’ Can only learn π if sample is skewed. Happens with probability at most exp(βπ 3 π΅) β’ At most ~ ππ distinct bad classifiers β’ Union bounding over all bad classifiers, learn bad classifier with prob. at most ππ exp(βπ 3 π΅) π β’ When π΅ = Ξ© 3 log π to get good classifier with high probability π Online PIP as Learning 2) Solving PIP via learning Improve thisβ¦ Probability of learning good classification: β’ Consider a classification π that overfills some budget: β’ Can only learn π if sample is skewed. Happens with probability at most exp(βπ 3 π΅) β’ At most ~ ππ distinct bad classifications β’ Union bounding over all bad classifications, learn desired good classification with prob. at least 1 β ππ exp(π 3 π΅) β’ When to get good classification with high probability Improved Learning Error β’ Idea 1: Covering bounds via witnesses (handling multiple bad classifiers at a time) +-witness: π is a +-witness of πβ for constraint π if 1) Columns picked by π β columns picked by πβ 2) Total occupation of constraint π by columns picked by π is > π΅ β-witness: similarβ¦ Lemma: Suppose there is a witness set of size π. Then probability of learning a bad classifier is ~π. exp(βπ 3 π΅) Total weight > π΅ Geometry of PIPs with Small Witness Set β’ For some PIPs, size of witness set is at least π > Ξ©(π) β’ Idea 2: Consider PIPs whose columns lie on few (πΎ) 1-d subspaces Geometry of PIPs with Small Witness Set β’ For some PIPs, size of witness set is at least π > Ξ©(π) β’ Idea 2: Consider PIPs whose columns lie on few (πΎ) 1-d subspaces π²=2 Lemma: For such PIPs, can find witness set of size π πΎ π π Geometry of PIPs with Small Witness Set β’ Covering bound + witness size: it suffices π΅ β₯ β’ Final step: Convert any PIP into one with πΎ = π Ξ© 3 log πΎ π π π π Algorithm 1 β π -competitive when π΅ = Ξ© , loses π value π2 log π3 π Conclusion β’ Guarantee for online PIPs independent of number of columns π β’ Asymptotically matches that for single constraint version [Kleinberg 05] β’ Ideas 1) Tailored covering bound based on witnesses 2) Analyze geometry of columns to obtain small witness set β Make the learning problem more robust Open problems 1) Obtain optimal π΅ β₯ Ξ© replacement [DJSW 11] 1 log π π2 ? Can do if sample columns with 2) Generalize to AdWords-type problem 3) Better online models: infinite horizon? less randomness? Thank you!