| Entrambe le parti precedenti la revisioneRevisione precedenteProssima revisione | Revisione precedente |
| magistraleinformatica:ad:ad_18:start [29/05/2019 alle 11:31 (6 anni fa)] – [Topics] Roberto Grossi | magistraleinformatica:ad:ad_18:start [24/04/2020 alle 07:13 (5 anni fa)] (versione attuale) – Roberto Grossi |
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| |10.04.2019| Case study on data streams (II): set membership and heavy hitters. | {{ :magistraleinformatica:ad:ad_18:data-stream-stats.pdf |slides}} {{ :magistraleinformatica:ad:ad_18:cms.zip | code}} | | |10.04.2019| Case study on data streams (II): set membership and heavy hitters. | {{ :magistraleinformatica:ad:ad_18:data-stream-stats.pdf |slides}} {{ :magistraleinformatica:ad:ad_18:cms.zip | code}} | |
| |12.04.2019| NP-hard problems: download file manager and the knapsack problem. Reduction from Partition to Knapsack (restriction). Dynamic programming algorithms for Knapsack: Case 1: integer weights, complexity O(nW). Case 2: integer values, complexity O(n<sup>2</sup>vmax). Examples. | {{ :magistraleinformatica:ad:ad_17:partition-knapsack.pdf | PDF}} [[https://repl.it/@grossiroberto/knapsack|code]] | | |12.04.2019| NP-hard problems: download file manager and the knapsack problem. Reduction from Partition to Knapsack (restriction). Dynamic programming algorithms for Knapsack: Case 1: integer weights, complexity O(nW). Case 2: integer values, complexity O(n<sup>2</sup>vmax). Examples. | {{ :magistraleinformatica:ad:ad_17:partition-knapsack.pdf | PDF}} [[https://repl.it/@grossiroberto/knapsack|code]] | |
| | |17.04.2019| Case study on approximation for graphs (max cut): single individual haplotypes reconstruction problem (HapCUT) | {{ :magistraleinformatica:ad:ad_18:hapcut.pdf |}} {{ :magistraleinformatica:ad:ad_18:bio.pdf |}}| |
| |30.04.2019| NP-hard problems: heuristics based on dynamic programming; approximation algorithms. Case study: knapsack problem. | [[http://www.dis.uniroma1.it/~ausiello/InfoTeoIIRM/book/chapter02.pdf| chapt.2: par. 2.1.1]] [[https://repl.it/@grossiroberto/knapsack|code]] | | |30.04.2019| NP-hard problems: heuristics based on dynamic programming; approximation algorithms. Case study: knapsack problem. | [[http://www.dis.uniroma1.it/~ausiello/InfoTeoIIRM/book/chapter02.pdf| chapt.2: par. 2.1.1]] [[https://repl.it/@grossiroberto/knapsack|code]] | |
| |03.05.2019| NP-hard problems: counting version (#P) based on dynamic programming, uniform random sampling of the feasible solutions. Case study: #knapsack problem. | {{ :magistraleinformatica:ad:ad_17:notesknapsack2.pdf |notes}} [[https://repl.it/@grossiroberto/ApproxKnapsack|code]] | | |03.05.2019| NP-hard problems: counting version (#P) based on dynamic programming, uniform random sampling of the feasible solutions. Case study: #knapsack problem. | {{ :magistraleinformatica:ad:ad_17:notesknapsack2.pdf |notes}} [[https://repl.it/@grossiroberto/ApproxKnapsack|code]] | |
| |06.05.2019| a | b| | |06.05.2019| Case study on approximation for metric k-center: Clustering and video summarization. | {{ :magistraleinformatica:ad:ad_18:clustering.pdf |}} {{ :magistraleinformatica:ad:ad_18:chapter2.pdf |}}| |
| |07.05.2019| NP-hard problems: fully polynomial-time randomized approximation schemes (FPRASs). Case study: #knapsack problem. | {{ :magistraleinformatica:ad:ad_17:notesknapsack2.pdf |notes}} [[https://repl.it/@grossiroberto/ApproxKnapsack|code]] | | |07.05.2019| NP-hard problems: fully polynomial-time randomized approximation schemes (FPRASs). Case study: #knapsack problem. | {{ :magistraleinformatica:ad:ad_17:notesknapsack2.pdf |notes}} [[https://repl.it/@grossiroberto/ApproxKnapsack|code]] | |
| |10.05.2019| General inapproximability results. Case study: travel salesman problem (TSP). 2-approximation algorithms for metric TSP, Local search. Greedy. Case study: max cut for graphs. Non-existence of PTAS. | [CLRS 35.2] {{:magistraleinformatica:alg2:algo2_14:lec02.pdf|Notes}} | | |10.05.2019| General inapproximability results. Case study: travel salesman problem (TSP). 2-approximation algorithms for metric TSP, Local search. Greedy. Case study: max cut for graphs. Non-existence of PTAS. | [CLRS 35.2] {{:magistraleinformatica:alg2:algo2_14:lec02.pdf|Notes}} | |
| |13.05.2019| Randomized approximation and derandomization: universal hash functions; conditional expectations. Case study: max-cut for graphs. | [[http://pages.cs.wisc.edu/~jyc/02-810notes/lecture19.pdf|sect. 3-4]] [[http://web.cs.iastate.edu/~pavan/633/lec14.pdf|sect. 1.1]] | | |13.05.2019| Randomized approximation and derandomization: universal hash functions; conditional expectations. Case study: max-cut for graphs. | [[http://pages.cs.wisc.edu/~jyc/02-810notes/lecture19.pdf|sect. 3-4]] [[http://web.cs.iastate.edu/~pavan/633/lec14.pdf|sect. 1.1]] | |
| |14.05.2019| a | b| | |14.05.2019| Case study on bottom-k sketches: approximate similarity searching | {{ :magistraleinformatica:ad:ad_18:06691730.pdf |}} {{ :magistraleinformatica:ad:ad_18:p371-thorup.pdf | only Sect.1}} {{ :magistraleinformatica:ad:ad_18:bio.pdf |}}| |
| |17.05.2019| Fixed-parameter tractable (FPT) algorithms. Kernelization. Bounded search tree. Case study: min-vertex cover in graphs. | [[https://www.mimuw.edu.pl/~malcin/book/parameterized-algorithms.pdf|sect. 2.2.1, 3.1]] | | |17.05.2019| Fixed-parameter tractable (FPT) algorithms. Kernelization. Bounded search tree. Case study: min-vertex cover in graphs. | [[https://www.mimuw.edu.pl/~malcin/book/parameterized-algorithms.pdf|sect. 2.2.1, 3.1]] | |
| |21.05.2019| Randomized FPT algorithms: color coding and randomized separation. Case study: longest path in graphs and subgraph isomorphism. | [[https://www.mimuw.edu.pl/~malcin/book/parameterized-algorithms.pdf|sect. 5.2, 5.3]] | | |21.05.2019| Randomized FPT algorithms: color coding and randomized separation. Case study: longest path in graphs and subgraph isomorphism. | [[https://www.mimuw.edu.pl/~malcin/book/parameterized-algorithms.pdf|sect. 5.2, 5.3]] | |
| |22.05.2019| a | b| | |22.05.2019| Case study on graphs: community detection is social networks | {{ :magistraleinformatica:ad:ad_18:sna.pdf |}} {{ :magistraleinformatica:ad:ad_18:nature03607.pdf |}} {{ :magistraleinformatica:ad:ad_18:nature03607-s1.pdf |}}| |
| |24.05.2019| Fine-grained algorithms. SETH conjecture and conditional lower bounds. Guaranteed heuristics. Case study: diameter in undirected unweighted graphs. | [[https://www.dropbox.com/s/zq0dklabkjyd302/20171212.pdf?dl=0|notes]] [[https://people.csail.mit.edu/virgi/ipec-survey.pdf|sect. 2.3, 2.4, 3, 4]]| | |24.05.2019| Fine-grained algorithms. SETH conjecture and conditional lower bounds. Guaranteed heuristics. Case study: diameter in undirected unweighted graphs. | [[https://www.dropbox.com/s/zq0dklabkjyd302/20171212.pdf?dl=0|notes]] [[https://people.csail.mit.edu/virgi/ipec-survey.pdf|sect. 2.3, 2.4, 3, 4]]| |
| |28.05.2019| Approximation in fine-grained algorithms and limitations. Case study: diameter in undirected unweighted graphs. Case study: communities detection in large graphs.| {{ :magistraleinformatica:ad:ad_17:diameterapprox.pdf | notes }} [[https://www.nature.com/articles/nature03607.pdf|paper]] [[https://images.nature.com/original/nature-assets/nature/journal/v435/n7043/extref/nature03607-s1.pdf|supplement]] | | |28.05.2019| Approximation in fine-grained algorithms and limitations. | {{ :magistraleinformatica:ad:ad_17:diameterapprox.pdf | notes }} | |
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