Improved Online Algorithms for Knapsack and GAP in the Random Order Model

The knapsack problem is one of the classical problems in combinatorial optimization: Given a set of items, each specified by its size and profit, the goal is to find a maximum profit packing into a knapsack of bounded capacity. In the online setting, items are revealed one by one and the decision, if the current item is packed or discarded forever, must be done immediately and irrevocably upon arrival. We study the online variant in the random order model where the input sequence is a uniform random permutation of the item set. We develop a randomized (1/6.65)-competitive algorithm for this problem, outperforming the current best algorithm of competitive ratio 1/8.06 [Kesselheim et al. SIAM J. Comp. 47(5)]. Our algorithm is based on two new insights: We introduce a novel algorithmic approach that employs two given algorithms, optimized for restricted item classes, sequentially on the input sequence. In addition, we study and exploit the relationship of the knapsack problem to the 2-secretary problem. The generalized assignment problem (GAP) includes, besides the knapsack problem, several important problems related to scheduling and matching. We show that in the same online setting, applying the proposed sequential approach yields a (1/6.99)-competitive randomized algorithm for GAP. Again, our proposed algorithm outperforms the current best result of competitive ratio 1/8.06 [Kesselheim et al. SIAM J. Comp. 47(5)]

New Results for the k-Secretary Problem

Suppose that n numbers arrive online in random order and the goal is to select k of them such that the expected sum of the selected items is maximized. The decision for any item is irrevocable and must be made on arrival without knowing future items. This problem is known as the k-secretary problem, which includes the classical secretary problem with the special case k=1. It is well-known that the latter problem can be solved by a simple algorithm of competitive ratio 1/e which is asymptotically optimal. When k is small, only for k=2 does there exist an algorithm beating the threshold of 1/e [Chan et al. SODA 2015]. The algorithm relies on an involved selection policy. Moreover, there exist results when k is large [Kleinberg SODA 2005]. In this paper we present results for the k-secretary problem, considering the interesting and relevant case that k is small. We focus on simple selection algorithms, accompanied by combinatorial analyses. As a main contribution we propose a natural deterministic algorithm designed to have competitive ratios strictly greater than 1/e for small k >= 2. This algorithm is hardly more complex than the elegant strategy for the classical secretary problem, optimal for k=1, and works for all k >= 1. We explicitly compute its competitive ratios for 2 <= k <= 100, ranging from 0.41 for k=2 to 0.75 for k=100. Moreover, we show that an algorithm proposed by Babaioff et al. [APPROX 2007] has a competitive ratio of 0.4168 for k=2, implying that the previous analysis was not tight. Our analysis reveals a surprising combinatorial property of this algorithm, which might be helpful for a tight analysis of this algorithm for general k

Best Fit Bin Packing with Random Order Revisited

Best Fit is a well known online algorithm for the bin packing problem, where a collection of one-dimensional items has to be packed into a minimum number of unit-sized bins. In a seminal work, Kenyon [SODA 1996] introduced the (asymptotic) random order ratio as an alternative performance measure for online algorithms. Here, an adversary specifies the items, but the order of arrival is drawn uniformly at random. Kenyon's result establishes lower and upper bounds of 1.08 and 1.5, respectively, for the random order ratio of Best Fit. Although this type of analysis model became increasingly popular in the field of online algorithms, no progress has been made for the Best Fit algorithm after the result of Kenyon. We study the random order ratio of Best Fit and tighten the long-standing gap by establishing an improved lower bound of 1.10. For the case where all items are larger than 1/3, we show that the random order ratio converges quickly to 1.25. It is the existence of such large items that crucially determines the performance of Best Fit in the general case. Moreover, this case is closely related to the classical maximum-cardinality matching problem in the fully online model. As a side product, we show that Best Fit satisfies a monotonicity property on such instances, unlike in the general case. In addition, we initiate the study of the absolute random order ratio for this problem. In contrast to asymptotic ratios, absolute ratios must hold even for instances that can be packed into a small number of bins. We show that the absolute random order ratio of Best Fit is at least 1.3. For the case where all items are larger than 1/3, we derive upper and lower bounds of 21/16 and 1.2, respectively.Comment: Full version of MFCS 2020 pape

Online Strip Packing with Polynomial Migration

We consider the relaxed online strip packing problem, where rectangular items arrive online and have to be packed into a strip of fixed width such that the packing height is minimized. Thereby, repacking of previously packed items is allowed. The amount of repacking is measured by the migration factor, defined as the total size of repacked items divided by the size of the arriving item. First, we show that no algorithm with constant migration factor can produce solutions with asymptotic ratio better than 4/3. Against this background, we allow amortized migration, i.e. to save migration for a later time step. As a main result, we present an AFPTAS with asymptotic ratio 1 + O(epsilon) for any epsilon > 0 and amortized migration factor polynomial in 1/epsilon. To our best knowledge, this is the first algorithm for online strip packing considered in a repacking model

Antimicrobial Peptides Originating from Expression Libraries of <i>Aurelia aurita</i> and <i>Mnemiopsis leidyi</i> Prevent Biofilm Formation of Opportunistic Pathogens

The demand for novel antimicrobial compounds is rapidly growing due to the rising appearance of antibiotic resistance in bacteria; accordingly, alternative approaches are urgently needed. Antimicrobial peptides (AMPs) are promising, since they are a naturally occurring part of the innate immune system and display remarkable broad-spectrum activity and high selectivity against various microbes. Marine invertebrates are a primary resource of natural AMPs. Consequently, cDNA expression (EST) libraries from the Cnidarian moon jellyfish Aurelia aurita and the Ctenophore comb jelly Mnemiopsis leidyi were constructed in Escherichia coli. Cell-free size-fractionated cell extracts (Klebsiella oxytoca and Staphylococcus epidermidis. Sequencing the respective activity-conferring inserts allowed for the identification of small ORFs encoding peptides (10–22 aa), which were subsequently chemically synthesized to validate their inhibitory potential. Although the peptides are likely artificial products from a random translation of EST inserts, the biofilm-preventing effects against K. oxytoca, Pseudomonas aeruginosa, S. epidermidis, and S. aureus were verified for five synthetic peptides in a concentration-dependent manner, with peptide BiP_Aa_5 showing the strongest effects. The impact of BiP_Aa_2, BiP_Aa_5, and BiP_Aa_6 on the dynamic biofilm formation of K. oxytoca was further validated in microfluidic flow cells, demonstrating a significant reduction in biofilm thickness and volume by BiP_Aa_2 and BiP_Aa_5. Overall, the structural characteristics of the marine invertebrate-derived AMPs, their physicochemical properties, and their promising antibiofilm effects highlight them as attractive candidates for discovering new antimicrobials