6.5220J/6.856J/18.416J Randomized Algorithms (Spring 2025) (original) (raw)

1

09/03

Introduction to Randomized Algorithms. Quicksort, BSP.

NB

L1

L01, L01

2

09/05

Min-cut, Complexity theory.

NB

L2

L02, L02

3

09/08

Adelman's theorem, Game tree evaluation.

NB

L3

L03, L03

4

09/10

Game theory, Lower bounds 1, Coupon collecting, Stable marriage

NB

L4

L04, L04

5

09/12

Deviations: Markov, Chebyshev. Balls in Bins.

NB

L5

L05, L05

6

09/15

Median finding. Pseudorandom numbers.

NB, NB

L6

L06, L06

7

09/17

Chernoff bound. Randomized routing.

NB

L7

L07, L07

09/19

No lecture

(Student Holiday)

8

09/22

The Power of Two Choices

NB

L8

L08, L08

9

09/24

Universal and Perfect Hashing (prerecorded)

NB, NB

L9

L09, L09

10

09/26

Two choices (cont). Cuckoo Hashing.

NB

L10

L10, L10

11

09/29

Consistent Hashing. Fingerprinting.

NB

L11

L11, L11

12

10/01

Text search. Bloom filters.

NB, NB

L12

L12, L12

13

10/03

Fingerprinting by polynomials, Perfect matching. Network coding.

NB

L13

L13, L13

14

10/06

Symmetry breaking. Parallel algorithms. Ethernet. Parallel perfect matching.

NB

L14

L14, L14

15

10/8

Shortest Paths (prerecorded).

NB

L16, L16

16

10/10

Parallel Maximal independent set. Derandomization.

NB

L16

L15, L15

10/13

No lecture

(Indigenous Peoples' Day)

17

10/15

Sampling: polling. Streaming algorithms: frequent items, sketches, distinct items.

NB, NB

18

10/17

Sampling: transitive closure. DNF counting, rare events.

NB, NB

L18

L18, L18

19

10/20

Counting versus generation. Minimum spanning tree.

NB

L19

L19, L19

20

10/22

Min-cuts: recursive contraction algorithm.

NB, NB

L20

L20, L20

21

10/24

Sampling for approximate min-cuts.

NB

L21

L21, L21

22

10/27

Sampling for exact min-cut. Network Reliability

NB

L22

L22, L22

23

10/29

Computational Geometry. Point Location in an arrangement of lines

NB

L23

L23,L23

24

10/31

Convex hull via randomized incremental construction. LP

NB, NB

L24

L24, L24

25

11/03

LP: sampling.

NB

L25

L25, L25

26

11/05

More sampling for LP. Seidel+hidden dimension.

NB

L26

L26, L26

27

11/07

LP: simplex. The probabilistic method.

NB, NB

L27

L27, L27

11/10

No lecture

(Student holiday)

28

11/12

Randomized rounding.

NB, NB

L28

L28

29

11/14

Embeddings.

NB

L29

30

11/17

Metric Embeddings 2.

NB

L30

31

11/19

Metric Embeddings; Markov chains 1.

NB, NB

L31

32

11/21

Markov chains 2: random walks.

NB

L32

33

11/24

Markov chains 3.

NB, NB

L33

34

11/26

Markov chains 4.

NB, NB

L34

11/28

No lecture

(Institute holiday)

35

12/01

36

12/03

37

12/05

38

12/08

Peer editing session.

(Required attendance!!)

39

12/10

(Last day of classes)

Below this point is subject to change