We have $k$ bins of capacity $c_i$ each. These bins appear in an online fashion, say on day $i$, bin $i$ appears. There are $l$ balls. Ball $j$ has weight $w_{ij}$ with bin $i$.

Each ball knows that there will be $k$ bins. On day $i$, bin $i$ appears and ball $j$ gets to know the bin and its capacity $c_i$. Also, ball $j$ gets to know $w_{i1},w_{i2},\ldots,w_{il}$; the weights of all balls with bin $i$ (including her weight of course). Ball $j$ must decide either to put herself into bin $i$ or not, once and forever (i.e., once decided, the game is over for ball $j$).

Each ball wants to be satisfied, that is, puts herself into a bin that has enough capacity. Ball $j$ does not know if some other balls have chosen bin $i$ or not. Also, Ball $j$ knows only the current information, does not know the weights of the balls with bins that will appear in the future.

Is there a way to find an online algorithm that maximizes the number of satisfied balls? I think this is somehow related to secretary problem but I am not sure how to prove this claim.

  • $\begingroup$ "Each ball wants to be satisfied, that is, puts herself into a bin that has enough capacity. The unknown here is that ball 𝑗 does not know if some other balls have chosen bin 𝑖 or not." It is unclear what this means. If all balls agree on some protocol in advance, you could use any online algorithm you want -- each ball would just simulate the algorithm to know what it (and the other balls) should do. $\endgroup$
    – Neal Young
    Nov 5 '19 at 23:54
  • $\begingroup$ If we assume that the balls cannot communicate with each other, does that make the problem clearer? $\endgroup$
    – zdm
    Nov 6 '19 at 0:25
  • $\begingroup$ @NealYoung I think it is better to see a special case of the problem. Say, when bin $i$ appears, it can contain at most one ball (we can choose the weights to satisfy this condition). This special case, I think, is the classical online matching problem. $\endgroup$
    – zdm
    Nov 6 '19 at 0:45
  • $\begingroup$ Suppose for now that there is a single algorithm deciding which balls go in each bin. Maybe using online-matching-like techniques you can prove that a greedy algorithm -- pack the most balls that will fit at each time step -- achieves a decent competitive ratio like 0.5. (Ignoring that it's NP-hard to compute.) $\endgroup$
    – usul
    Nov 6 '19 at 0:56
  • $\begingroup$ Say $k=2$ and $l=2$. Say bin 1 has capacity $c_1=2$ and bin 2 has capacity $c_2=1$. Say when bin 1 appears, we have $(w_{11},w_{12})=(1,2)$ and an online algorithm puts ball 1 into bin 1. Now, when bin 2 appears, say we have $(w_{21},w_{22})=(1,2)$. The online algorithm cannot put any ball in bin 2 (it has already put ball 1 into bin 1 and ball 2 cannot be put into bin 2). However, ball 1 can be put into bin 2 and ball 2 can be put into bin 1. I meant to say that future weights and future capacities are not known. $\endgroup$
    – zdm
    Nov 6 '19 at 2:47

Per @usul's suggestion, consider the following online greedy algorithm $A$. When bin $i$ is revealed, $A$ considers all subsets $S$ of not-yet assigned balls such that all balls in $S$ can fit together in bin $i$. It chooses any largest such set, say $A_i$, then assigns all balls in $A_i$ to bin $i$.

The post asks for an algorithm that each ball can use independently. To achieve this, just have each ball independently simulate $A$, and decide to join a given bin only if $A$ says it should.

Lemma 1. This greedy algorithm is 0.5-competitive.

Proof. Let $A^* = \bigcup_{i} A_i$ denote the set of balls assigned by $A$. Let $O_i$ denote the set of balls assigned by OPT to bin $i$. Let $O^* = \bigcup_i O_i$ be the set of all balls assigned by OPT.

The set $O_i \setminus A^*$ is one possible set that $A$ could have assigned to bin $i$. Set $A_i$ is at least as large as this set, so $|O_i \setminus A^*| \le |A_i|$. Summing over $i$ gives $|O^*\setminus A^*| \le |A^*|$. That is, the number of balls assigned by OPT but not $A$ is at most the number assigned by $A$. This implies the desired bound. Specifically, as $O^* \subseteq A^* \cup (O^*\setminus A^*)$, it follows that $$|O^*| \le |A^*| + |O^*\setminus A^*| \le |A^*| + |A^*| = 2|A^*|.~~~~~~~~\Box$$

If you want a polynomial-time algorithm, you can modify $A$ to choose each set $A_i$ so that it is within a factor of $1-\epsilon$ of the largest possible. (E.g., to achieve $\epsilon=1/2$ just order the not-yet-assigned balls by increasing $w_{ij}$, and take as many as will fit in bin $i$. Or use a PTAS at each step to achieve any fixed $\epsilon>0$.) Then $|O_i \setminus A^*| \le |A_i|/(1-\epsilon)$ and the algorithm will be $(1-\epsilon)/(2-\epsilon)$-competitive. (E.g. $1/3$-competitive for $\epsilon=1/2$.)

And just for the record, per OP's comment:

Lemma 2. No deterministic online algorithm has competitive ratio more than 0.5.

Proof. Consider the instance with two balls and two capacity-1 bins, where each ball has weight 1 for bin 1, but only the ball put by the online algorithm in bin 1 can fit in bin 2.$~~~\Box$

  • 1
    $\begingroup$ The reason for 1/2 in this case is known to follow from local-greedy for submodular function maximization subject to a partition matroid constraint. It may shed light on more general problems. I will post a pointer when I get a chance. $\endgroup$ Nov 6 '19 at 17:39
  • 1
    $\begingroup$ Related to my other comment, here are talk slides from 2006 where I explain the connection between multiple knapsack, generalized assignment and local-greedy for submodular function maximization. Some may find this connection helpful. chekuri.cs.illinois.edu/talks/crm_submodular.pdf $\endgroup$ Nov 6 '19 at 20:05

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