Lecture Algorithm design

Lecture Algorithm design "Representative problems" include all of the following: Matching med-school students to hospitals, stable matching problem, perfect matching, unstable pair, stable matching problem, stable roommate problem, Gale-Shapley deferred acceptance algorithm,...and another content.

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The main contents of lecture Algorithm design chapter 2 "Algorithm analysis" include all of the following: Computational tractability, asymptotic order of growth, survey of common running times.

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Lecture Algorithm design - Chapter 4: Greedy Algorithms II include all of the following: Dijkstra's algorithm; minimum spanning trees; Prim, Kruskal, Boruvka; single-link clustering; min-cost arborescences.

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The following will be discussed in this chapter: Basic definitions and applications, graph connectivity and graph traversal, testing bipartiteness, connectivity in directed graphs, DAGs and topological ordering.

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Lecture Algorithm design - Chapter 4: Greedy Algorithms I include all of the following: Coin changing, interval scheduling, scheduling to minimize lateness, optimal caching.

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The following will be discussed in lecture Algorithm design - Chapter 5: Divide and Conquer II: Master theorem, integer multiplication, matrix multiplication, convolution and FFT. inviting you refer.

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Lecture Algorithm design - Chapter 6: Dynamic programming I include all of the following: Weighted interval scheduling, segmented least squares, knapsack problem, RNA secondary structure.

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Lecture Algorithm design - Chapter 5: Divide and conquer I include all of the following: Mergesort, counting inversions, closest pair of points, randomized quicksort, median and selection. For more details, inviting you refer to the above lesson.

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Lecture Algorithm design - Chapter 6: Dynamic programming II include all of the following: Sequence alignment, hirschberg's algorithm, Bellman-Ford algorithm, distance vector protocols, negative cycles in a digraph.

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Lecture Algorithm design - Chapter 7 "Network flow I" include all of the following: Max-flow and min-cut problems, Ford-Fulkerson algorithm, max-flow min-cut theorem, capacity-scaling algorithm, shortest augmenting paths, blocking-flow algorithm, unit-capacity simple networks.

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Chapter 7 discusses how to design a relational database schema based on a conceptual schema design. This chapter presented a high-level view of the database design process, and in this chapter we focus on the logical database design or data model mapping step of database design. We present the procedures to create a relational schema from an Entity-Relationship (ER) or an Enhanced ER (EER) schema.

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Chapter 11 present the contents: Designing a set of relations, properties of relational decompositions, algorithms for relational database schema, multivalued dependencies and fourth normal form, join dependencies and fifth normal form, inclusion dependencies, other dependencies and normal forms.

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Lecture Operating System: Chapter 04 - Memory management presented basic memory management, swapping, virtual memory, page replacement algorithms, modeling page replacement algorithms, design issues for paging systems, implementation issues, segmentation.

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Web search engine: Markov chain theory Data Mining, Machine Learning: Data mining, Machine learning: Stochastic gradient, Markov chain Monte Carlo, Image processing: Markov random fields, Design of wireless communication systems: random matrix theory, Optimization of engineering processes: simulated annealing, genetic algorithms, Finance (option pricing, volatility models): Monte Carlo, dynamic models, Design of atomic bomb (Los Alamos): Markov chain Monte Carlo.

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