magistraleinformatica:ad:ad_19:start
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Queste sono le differenze tra la revisione selezionata e la versione attuale della pagina.
Entrambe le parti precedenti la revisioneRevisione precedenteProssima revisione | Revisione precedente | ||
magistraleinformatica:ad:ad_19:start [12/05/2020 alle 06:45 (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? | ||
- | - Join the class on **Google Classroom** (use Android/iOS or connect to the [[https:// | + | - Join the class on Google Classroom (use Android/iOS or connect to the [[https:// |
- Click on the link for streaming on [[https:// | - Click on the link for streaming on [[https:// | ||
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|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< | |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< | ||
|28.04.2019| NP-hard problems: heuristics based on dynamic programming; | |28.04.2019| NP-hard problems: heuristics based on dynamic programming; | ||
- | + | |30.04.2019| Clique-based social network analysis (seminar by F.Geraci) | classroom drive | | |
- | 30 | + | |
|05.05.2020| NP-hard problems: counting version (#P) based on dynamic programming, | |05.05.2020| NP-hard problems: counting version (#P) based on dynamic programming, | ||
|07.05.2020| NP-hard problems: fully polynomial-time randomized approximation schemes (FPRASs). Case study: #knapsack problem. | {{ : | |07.05.2020| NP-hard problems: fully polynomial-time randomized approximation schemes (FPRASs). Case study: #knapsack problem. | {{ : | ||
|12.05.2020| General inapproximability results. Case study: travel salesman problem (TSP). | |12.05.2020| General inapproximability results. Case study: travel salesman problem (TSP). | ||
|14.05.2020| Randomized approximation and derandomization: | |14.05.2020| Randomized approximation and derandomization: | ||
- | |15.05.2020| Case study on bottom-k sketches: approximate similarity searching | {{ : | + | |15.05.2020| Fixed-parameter tractable (FPT) algorithms. Kernelization. Bounded search tree. Case study: min-vertex cover in graphs. |
- | |19.05.2020| Fixed-parameter tractable (FPT) algorithms. Kernelization. Bounded search tree. Case study: min-vertex cover in graphs. | + | |19.05.2020| Randomized FPT algorithms: color coding and randomized separation. Case study: longest path in graphs and subgraph isomorphism. | [[https:// |
- | |21.05.2020| Randomized FPT algorithms: color coding and randomized separation. Case study: longest path in graphs and subgraph isomorphism. | [[https:// | + | |
magistraleinformatica/ad/ad_19/start.1589265948.txt.gz · Ultima modifica: 12/05/2020 alle 06:45 (5 anni fa) da Roberto Grossi