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--- magistraleinformatica:ad:ad_19:start [25/04/2020 alle 11:36 (6 anni fa)] – Roberto Grossi
+++ magistraleinformatica:ad:ad_19:start [07/07/2020 alle 07:59 (6 anni fa)] (versione attuale) – Roberto Grossi
@@ Linea 11: / Linea 11: @@
 You student, what can you do next for getting a lecture?
-  - 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.
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 |23.04.2020| Networked data and randomized min-cut algorithm for graphs. | {{:magistraleinformatica:alg2:algo2_15:mincut1.pdf| par.1.1}} |
 |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]] |