| Entrambe le parti precedenti la revisioneRevisione precedenteProssima revisione | Revisione precedente |
| magistraleinformatica:ad:ad_19:start [24/04/2020 alle 05:04 (5 anni fa)] – Roberto Grossi | magistraleinformatica:ad:ad_19:start [07/07/2020 alle 07:59 (5 anni fa)] (versione attuale) – Roberto Grossi |
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| You student, what can you do next for getting a lecture? | You student, what can you do next for getting a lecture? |
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| - Join the class on **Google Classroom** (use Android/iOS or connect to the [[https://classroom.google.com/u/1/c/NjI0NjI4NjExNzRa|Algorithm Design link]]), and use the code below: {{:magistraleinformatica:ad:ad_19:code.jpg?400|}}\\ \\ | - Join the class on Google Classroom (use Android/iOS or connect to the [[https://classroom.google.com/u/1/c/NjI0NjI4NjExNzRa|Algorithm Design link]]). |
| - Click on the link for streaming on [[https://meet.google.com/rco-fojo-cqn|Google Meet]] for attending the classes. Please note that we //keep our schedule for time//, the only difference is that you have connect to the link instead of physically coming to the room. | - Click on the link for streaming on [[https://meet.google.com/rco-fojo-cqn|Google Meet]] for attending the classes. Please note that we //keep our schedule for time//, the only difference is that you have connect to the link instead of physically coming to the room. |
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| |13.03.2020| Multiplicative universal hashing.| [[https://arxiv.org/abs/1504.06804|Sect. 2.3]]| | |13.03.2020| Multiplicative universal hashing.| [[https://arxiv.org/abs/1504.06804|Sect. 2.3]]| |
| |17.03.2020| Data streaming and sketching algorithms: approximate counters (part 1).| [[https://www.sketchingbigdata.org/fall17/lec/lec1.pdf|Sect. 3-5]]| | |17.03.2020| Data streaming and sketching algorithms: approximate counters (part 1).| [[https://www.sketchingbigdata.org/fall17/lec/lec1.pdf|Sect. 3-5]]| |
| |19.03.2020| Case study on hashing: rsync and file synchronization using hash functions.| {{ :magistraleinformatica:ad:ad_19:20200319.pdf |slides }}| | |19.03.2020| Case study on hashing: rsync and file synchronization using hash functions (seminar by F.Geraci).| classroom drive| |
| |20.03.2020| Sketching algorithms: approximate counters (part 2).| [[https://www.sketchingbigdata.org/fall17/lec/lec1.pdf|Sect. 3-5]]| | |20.03.2020| Sketching algorithms: approximate counters (part 2).| [[https://www.sketchingbigdata.org/fall17/lec/lec1.pdf|Sect. 3-5]]| |
| |24.03.2020| Sketching algorithms: approximate counters (part 3).| [[https://www.sketchingbigdata.org/fall17/lec/lec1.pdf|Sect. 3-5]]| | |24.03.2020| Sketching algorithms: approximate counters (part 3).| [[https://www.sketchingbigdata.org/fall17/lec/lec1.pdf|Sect. 3-5]]| |
| |27.03.2020| Count-Min sketches for frequent elements.| {{http://dimacs.rutgers.edu/~graham/pubs/papers/cm-full.pdf| sects.1-3, 4.1}} [[https://sites.google.com/site/countminsketch/|Site]] {{:magistraleinformatica:alg2:algo2_12:count-min-sketch.pdf|Notes}} [[https://repl.it/Lvob/3|code]]| | |27.03.2020| Count-Min sketches for frequent elements.| {{http://dimacs.rutgers.edu/~graham/pubs/papers/cm-full.pdf| sects.1-3, 4.1}} [[https://sites.google.com/site/countminsketch/|Site]] {{:magistraleinformatica:alg2:algo2_12:count-min-sketch.pdf|Notes}} [[https://repl.it/Lvob/3|code]]| |
| |31.03.2020| Integer counters and range queries with Count-Min Sketches: implementation and analysis. | {{https://7797b024-a-62cb3a1a-s-sites.googlegroups.com/site/countminsketch/cm-latin.pdf| sects.3-4}} | | |31.03.2020| Integer counters and range queries with Count-Min Sketches: implementation and analysis. | {{https://7797b024-a-62cb3a1a-s-sites.googlegroups.com/site/countminsketch/cm-latin.pdf| sects.3-4}} | |
| |02.04.2020||| | |02.04.2020| Data stream statistics - part 1 (seminar by F.Geraci)| classroom drive| |
| |03.04.2020||| | |03.04.2020| Document resemblance with MinHash, k-sketches and the Jaccard similarity index. Azuma-Hoeffding bound. Triangle counting. | [[http://gatekeeper.dec.com/ftp/pub/dec/SRC/publications/broder/positano-final-wpnums.pdf|paper]] [[http://cs.brown.edu/courses/cs253/papers/nearduplicate.pdf|paper]] [[http://homes.cs.washington.edu/~jrl/cs525/scribes08/lec10.pdf|Azuma-Hoeffding]] [[https://repl.it/MDNO/3|code]]| |
| |07.04.2020||| | |07.04.2020|Unifying view of sketches: min-k, bottom-k, threshold-t. Jaccard example. | classroom drive | |
| |09.04.2020||| | |09.04.2020|Distance distribution in networks: approximation with random sampling and sketches| classroom drive | |
| |16.04.2020||| | |16.04.2020| Data stream statistics - part 2 (seminar by F.Geraci). | classroom drive| |
| |17.04.2020||| | |17.04.2020| 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]]| |
| |21.04.2020||| | |21.04.2020| Approximation in fine-grained algorithms and limitations. Case study: diameter in undirected unweighted graphs. | {{ :magistraleinformatica:ad:ad_17:diameterapprox.pdf | notes }} | |
| |23.04.2020||| | |23.04.2020| Networked data and randomized min-cut algorithm for graphs. | {{:magistraleinformatica:alg2:algo2_15:mincut1.pdf| par.1.1}} | |
| |24.04.2020||| | |24.04.2020| 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]] | |
| | |28.04.2019| 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| Clique-based social network analysis (seminar by F.Geraci) | classroom drive | |
| | |05.05.2020| 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]] | |
| | |07.05.2020| 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]] | |
| | |12.05.2020| 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}} | |
| | |14.05.2020| 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]] | |
| | |15.05.2020| 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]] | |
| | |19.05.2020| 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]] | |
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