1 / 18

Parameterized Approximation Scheme for the Multiple Knapsack Problem

CPSC669 Term Project—Paper Reading. Parameterized Approximation Scheme for the Multiple Knapsack Problem. Klaus Jansen SODA 2009. Yan Lu 2011-04-26. Outline. 1. Problem Definition 2. Approximation Scheme 2.1 Instances with similar capacities 2.2 General cases . 1 Problem Definition.

alma
Download Presentation

Parameterized Approximation Scheme for the Multiple Knapsack Problem

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. CPSC669 Term Project—Paper Reading Parameterized Approximation Scheme for the Multiple Knapsack Problem Klaus Jansen SODA 2009 Yan Lu 2011-04-26

  2. Outline 1. Problem Definition 2. Approximation Scheme • 2.1 Instances with similar capacities • 2.2 General cases

  3. 1 Problem Definition • Multiple Knapsack Problem (MKP): • Given a set A of n items and a set B of m bins (knapsacks) such that each item aA has a size size(A) and a profit value profit(a), and each bin bB has a capacity c(b), the goal is to find a subset U Aof maximum total profit such that U can be packed into B without exceeding the capacities. • A generalization of the classical knapsack problem • Strongly NP hard  no FPTAS • Previous result: PTAS by Chekuri and Khanna [4] • This paper:

  4. Outline 1. Problem Definition 2. Approximation Scheme • 2.1 Instances with similar capacities • 2.2 General instances

  5. 2 Approximation Scheme 2.1 Instances with similar capacities • Let be the different capacities in an instance of MKP. Suppose for each capacity , , there are . • Approximation algorithm: • (1) Linear program relaxation • (2) Rounding the LP solution • (3) Selecting the Items • (4) Strip packing • (5) Shifting Technique

  6. (1) Linear Program Relaxation • Main idea: select a fractionalxi piece of each item ai, distribute these pieces as even smaller pieces among all bins (fractional). • The LP is a relaxation of MKP • Could be approximately solved as a max-min resource sharing problem [9], and solution has an objective value of at least 1-3α times OPT(LP), or OPT(A,B)

  7. (2) Rounding the LP solution • Define as the piece of item ai assigned to group l. • Think large pieces () ) as rectangles with width size(ai) and height • Stack all these rectangles ordered by widths, stack height • Divide each stack into parts • Let Klbe the set of pieces lying in more than 1 parts, notice • Remove items corresponding to Kl

  8. (2) Rounding the LP solution (cont’d) • Let Sl,jbe the set of items ai that have a piece in part j of stack l. • For small pieces, compute the total area allocated to group l Area(l) = • Let S0,j be the set of ai with a small piece in the sum above • Set a LP to round over groups: • This LP has a feasible solution

  9. (2) Rounding the LP solution (cont’d) • Lemma 2.1The solution can be rounded into another solution where each set has at most items. • NoteLl is the set of items with fractional pieces in more than 1 groups • After rounding, we have a unique assignment of items to groups, i.e. each item is assigned to exactly one group l and one part j.

  10. (3) Selecting the Items • In the rounding result, items selected are generally fractional. • Select complete items with near optimum profit, by solving classical fractional knapsack problems for each group and each part: • take as size of an item the value 1 and as profit the original profit profit(ai) • the capacity of the knapsack equals to , i.e. height of a part in Stack l • In overall solution A, at most one fractional item per group and part. • Let Ml be the set of selected fractional items in group l. • All other items are selected completely.

  11. (4) Strip packing • Lemma 2.2The set can be packed into bins of capacity cl for each l. • Using strip packing algorithm by Kenyon and Remila [10] • This implies (using the shifting technique described below) that most of the items (with near optimum profit) can be packed into ml bins. (5) Shifting technique • Recall : items removed from LP solution. • Let . Then • Lemma 2.3We can select a subset with profit at least that can be packed into ml bins.

  12. Entire algorithm • (1) Solve the LP approximately whose objective value is at least (1−3α) times the optimum LP value • (2) Build t stacks of wide rectangles and sets with narrow rectangles, split the stacks into 1/δ2parts and round the rectangles over the groups. Then select the items via solving fractional knapsack problems and store the fractional items • (3) Use the strip packing algorithm by Kenyon and Remila for each group to pack the items into bins • (4) Apply the shifting strategy to select subsets that can be packed into ml bins.

  13. 2 Approximation Scheme 2.2 General instances • When number of bins smaller than , they presented an approximation scheme with running time • From now on, suppose number of bins larger than • (1) Modify structure of bins • (2) Modify structure of high profit items • (3) Overall algorithm

  14. (1) Modify the bins • Order the bins according to capacities • k groups with bins, with • One group with bins • Lemma 2.4We can transform the optimum solution for an instance (A, B) such that the k-th group of bins is not used and the profit loss is at most

  15. (2) Modify the high profit items • Let APP(A, B) be the profit of the greedy algorithm, • Consider only items with large profit where γ is the number of bins in B2 • Case 1: , at most items in the instance. We could pack all of them in B2. Denote them with set SmHi • Case 2: , for each rounded profit value , choose at most smallest items with profit round(k) and store in A(k) • Guess a subset with high profit items for the bins in B2, through enumeration.In total, only need to consider choices.

  16. (3) Overall algorithm • Compute approximate solution • Modify structure of bins • Compute the set SmHi and sets A(k) • For each subset • Test whether Aguessfits into B2; if not, discard it • If yes, take a feasible placement of Aguessinto B2 and set up a linear program to select the remaining items (similar to the instances with similar capacities) • Place the selected items into bins • Take a solution among all feasible choices Aguess with maximum total profit. • Total running time

  17. [1] E. Balas and E. Zemel: An algorithm for large zero-one knapsack problems, Operations Research, 28 (1980), 1130-1154. [2] M. Blum, R.W. Floyd, V. Pratt, R.L. Rivest, and R.E. Tarjan: Time bounds for selection, Journal of Computer and System Sciences, 7 (1973), 448-461. [3] C. Chekuri and S. Khanna, A PTAS for the multiple knapsack problem, Proceedings of ACM-SIAM Symposium on Discrete Algorithms, SODA 2000, 213-222. [4] C. Chekuri and S. Khanna: A polynomial time approximation scheme for the multiple knapsack problem, SIAM Journal on Computing, 35 (2006), 713-728. [5] F. Diedrich, K. Jansen, F. Pascual, and D. Trystram: Approximation algorithms for scheduling wit reservations, Proceedings of Conference on High Performance Computing, HiPC 2007, 297-307. [6] D. Dor and U. Zwick: Selecting the median, SIAM Journal on Computing, 28 (1999), 1722-1758. [7] R. Downey: Parametrized complexity for the skeptic, Proceedings of IEEE Conference on Computational Complexity, CCC 2003, 147-169. [8] M.R. Fellows: Blow-ups, win/win’s, and crown rules: some new directions in FPT, Proceedings of Workshop on Graph Theoretical Concepts in Computer Science, WG 2003, LNCS 2880, 1-12. [9] M.D. Grigoriadis, L.G. Khachiyan, L. Porkolab and J. Villavicencio: Approximate max-min resource sharing for structured concave optimization, SIAM Journal on Optimization, 41 (2001), 1081-1091. [10] C. Kenyon and E. Remila: Approximate strip packing, Mathematics of Operations Research, 25 (2000), 645-656. [11] H. Kellerer: A polynomial time approximation scheme for the multiple knapsack problem, Proceedings of Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 1999, LNCS 1671, 51-62. [12] H. Kellerer, U. Pferschy, and D. Pisinger: Knapsack Problems, Springer Verlag, Berlin, 2004. [13] E.L. Lawler: Fast approximation algorithms for knapsack problems, Mathematics of Operations Research, 4 (1979), 339-356. [14] J.K. Lenstra, D.B. Shmoys, and E. Tardos: Approximation algorithms for scheduling unrelated parallel machines, Mathematical Programming, 24 (1990), 259-272. [15] S. Martello and P. Toth: Knapsack Problems: Algorithms and Computer Implementations, John Wiley and Sons, New York, 1990. [16] D. Marx: Parametrized complexity and approximation algorithms, The Computer Journal, 51 (2008), 60-78. [17] S.A. Plotkin, D.B. Shmoys, and E. Tardos: Fast approximation algorithms for fractional packing and covering problems, Mathematics of Operations Research, 20 (1995), 257-301. [18] M. Scharbrodt, A. Steger, and H. Weisser: Approximability of scheduling with fixed jobs, Proceedings of ACM-SIAM Symposium on Discrete Algorithms, SODA 1999, 961-962. [19] D.B. Shmoys and E. Tardos: An approximation algorithm for the generalized assignment problem, Mathematical programming, Series A, 62 (1993), 461-474.

  18. Thanks!

More Related