1. Improved Competitive Ratios for Submodular Secretary Problems ? Moran Feldman Roy SchwartzJoseph (Seffi) Naor Technion – Israel Institute of Technology

2. Outline The classical secretary problem – Algorithm – Alternative arrival process Prelimineries – Set functions – Competitive ratio Submodular Secretary Problems – State of the art and our results – Partition matroid – Uniform matroid 2

3. The Secretary Problem n secretaries, each with a unique internal value v i. The Secretaries are interviewed one after the other, in a random order. In the interview: – The value of the secretary is revealed. – We can hire the secretary, or dismiss her. – In both cases, the decision is final. 3 We can succeed with probability 1/e! [Bruss84] v1v1 v2v2 v3v3 v4v4 v5v5 v6v6 v7v7 v8v8 v9v9  Goal: Hire the best secretary with high probability. Question: How good can we do?

4. The Algorithm 4 Algorithm 1.Inspect the first n/e secretaries; let v be the value of the best secretary among these. 2.Interview the rest of the secretaries; hire the first one with value over v.  It can be shown that this algorithm succeeds with probability at least 1/e. [Bruss84]

5. The Arrival Process Secretaries arrive in a random order (random permutation). Alternative Arrival Process Secretaries arrive at random times within the range [0, 1]. Equivalence of the Arrival Processes  Random arrival times induce a random permutation.  Choose n random arrival times, sort them, and assign them to the secretaries upon arrival. 5 1/3 1/2 4/5 Claim Presenting and analyzing algorithms for secretary problems under this alternative arrival process is easier.

6. Alternative Algorithm Alternative Algorithm for the Secretary Problem 1.Inspect all secretaries till time t = 1/e; let v be the best secretary among these. 2.Interview the rest of the secretaries; hire the first one with value over v. Remarks This algorithm is not identical to the previous one. – For example, this algorithm might inspect no secretaries in step 1. Since this is a very simple case, the algorithms and analysis for both arrival processes are very similar. For more complex cases, the alternative arrival process yields cleaner proofs. 6

7. Analysis of the Alternative Algorithm Theorem The alternative algorithm succeeds with probability at least 1/e. Proof Let x be the arrival time of the best secretary. If x ≤ t, the algorithm fails. If x > t, the algorithm succeeds if: – The best secretary in the range [0,x) arrives before time t. – Success probability: t/x x ~ U(0, 1), thus, the success probability is: 7 t x Fail Success

8. Set Functions Definition Given a ground set E, a set function f : 2 E   assigns a number to every subset of the ground set. Properties 8 PropertyDefinition Normalization f(  ) = 0 Monotonicity For every two sets A  B  E: f(A)  f(B) Submodularity For all sets A, B  E: f(A) + f(B)  f(A  B) + f(A  B)

9. The Improtance of Submodularity Alternative (more intuitive) Definition A function f is submodular if for sets A  B  E and e  B: f e (A)  f e (B). The “economy of scale” feeling of this definition made submodular functions common in economics and game theory. Submodular Function in Combinatorics Submodular functions appear frequently in combinatorial settings. Here are two simple examples: 9 Ground SetSubmodular Function Nodes of a graphThe number of edges leaving a set of nodes. Collection of setsThe number of elements in the union of a sub-collection.

10. Variants of the Secretary Problem Many variants of the secretary problem were considered. For example: – Maximize the probability of every secretary to get hired, given that this probability must be independent of the arrival time of the secretary. [BJS10] – Hire up to k secretaries with maximum total value. [Kleinberg05] – Hire a set of secretaries with maximum total value which is independent in a given matroid. [BIK07] Two kinds of objectives: – Complete the job with maximum probability. – Maximize a given objective functions. For the second kind of objectives, algorithms are usually evaluated by their competitive ratio. 10

11. Competitive Ratio Notation I – An instance of a secretary problem. ALG( I ) – The value of an algorithm ALG on I. Notice that ALG( I ) is a random variable of: – The arrival times. – The randomness of ALG itself. OPT( I ) – The value of the optimal offline algorithm on I. – An offline algorithm know all secretaries from the beginning. Competitive Ratio Definition: Remark: This definition of the competitive ratio is adjusted for secretary problems. 11

12. Submodular Secretary Problems What is it? A secretary problem where the objective function is to maximize a given submodular objective function. What problems were considered? NMS – Normalized Monotone Submodular NS – Nonnegative Submodular 12 ConstraintObjective FunctionPrevious ResultOur Result Partition matroid constraint (given k predefined sets, hire at most one secretary from each set) NS5.55 ∙ 10 -5 [GRST10]- NMS5.55 ∙ 10 -5 [GRST10]0.153 Uniform matroid constraint (hire up to k secretaries) NS0.0169 [BHZ10]- NMS0.0903 [BHZ10]0.170 Knapsack constraintNMS0.0104 [BHZ10]0.0184

13. The Secretary Problem over a Partition Matroid Definition Each secretary belongs to one of k sets G 1,G 2,…,G k. At most one secretary of each set can be hired. Objective: maximize a given objective function over the set of hired secretaries. Algorithm for linear objectives Apply the algorithm for the standard secretary problem to each set G i, independently. Theorem The above algorithm is e -1 -competitive. Proof Follows from the linearity of the expectation since each set is a completely independent instance of the secretary problem. 13 Remark Such a “black box” reduction works only for the alternative arrival process.

14. The Secretary Problem over a Partition Matroid (cont.) Algorithm for submodular objectives 1.Inspect all secretaries till time t = ½. 2.Interview the rest of the secretaries. Hire secretary s from set G i, if:  No secretary of G i was hired before.  Among the secretaries of G i seen so far, s maximizes f s (R), where R is the set of secretaries hired so far. Remark For linear functions the above algorithm degenerates to the previous one, with a different threshold time. Theorem The above algorithm is (1 – ln 2) / 2 ≈ 0.153-competitive. 14

15. The Secretary Problem over a Uniform Matroid Definition Hire at most k secretaries. Objective: maximize a given objective function over the set of hired secretaries. Algorithm for linear objectives Divide time into k intervals. Apply the algorithm for the classical secretary problem to each interval separately. Theorem The above algorithm is (e – 1)∙e -2 -competitive. Remarks The above algorithm is the analog for the alternative arrival process of the algorithm of [BHZ10]. Better algorithms are known for the linear case. 15

16. The Secretary Problem over a Uniform Matroid (cont.) Lemma Let s i be the best secretary in the i th interval, then: Proof The probability that an interval will get no secretary of OPT is: For every interval i, let us select a random representative r i from the secretaries of OPT in interval i. 16 Interval i

17. The Secretary Problem over a Uniform Matroid (cont.) There are k intervals and in expectation at least (1-e -1 )k representatives. By symmetry, every secretary is a representative with probability at least 1-e -1. By linearity of the expectation: Proof of the Theorem [the algorithm’s compeitive ratio] Fix the distribution of secretaries among intervals. In expectation, the value collected in the i th interval is at least e -1 ∙ w(s i ). The expected value of the algorithm is at least: 17

18. The Secretary Problem over a Uniform Matroid (cont.) Algorithm for submodular objectives Same as the algorithm for linear objectives. Inside every intervals, consider the marginal contributions of the secretaries, instead of their weights. Remark For linear functions the above algorithm degenerates to the previous one. Theorem The above algorithm is (e – 1) / (e 2 + e) ≈ 0.170- competitive. 18