# CLRS 3: Selected

715 2012-12-22 19:12## VII Selected Topics

Introduction 769

### 27 Multithreaded Algorithms 772

- serial algorithms
- parallel algorithms
- No agreement on a single architectural model for parallel computers
- shared memory (multicore)
-> complex schedule and load-balance -> concurrency platforms**static threads****dynamic multithreading platform**- 2 features: 1. nested parallelism. Subroutine can be spawned. (divide-and-conquer) 2. parallel loops.
- 3 keywords: parallel, spawn, sync.
- Platforms: Cilk, Cilk++, OpenMP, Task Parallel Library, Threading Building Blocks, Intel TBB.

- distributed memory

- shared memory (multicore)

- No agreement on a single architectural model for parallel computers

#### 27.1 The basics of dynamic multithreading

- The keyword
**spawn**does not say, however, that a procedure must execute concurrently with its spawned children, only that it*may*. It is up to a scheduler at run time. - A procedure cannot safely use the values returned by its spawned children until after it executes a sync statement

算Fibonacci，recursion 的时候，spawn一个，自己算一个，sync，对这两个结果求和。

##### A model for multithreaded execution

- Linear speedup: T1/Tp = Θ§; Perfect linear speedup: T1/Tp = P

27.2 Multithreaded matrix multiplication 792 27.3 Multithreaded merge sort 797

### 28 Matrix Operations 813

- numerical stability: limited precision
- numerically unstable

28.1 Solving systems of linear equations 813 28.2 Inverting matrices 827 28.3 Symmetric positive-definite matrices and least-squares approximation 832

### 29 Linear Programming 843

29.1 Standard and slack forms 850 29.2 Formulating problems as linear programs 859 29.3 The simplex algorithm 864 29.4 Duality 879 29.5 The initial basic feasible solution 886

### 30 Polynomials and the FFT 898

- Fast Fourier transform, or FFT, can reduce the time to multiply polynomials to Θ(n lg n).

30.1 Representing polynomials 900 30.2 The DFT and FFT 906 30.3 Efficient FFT implementations 915

### 31 Number-Theoretic Algorithms 926

- Cryptographic schemes based on large prime numbers.

31.1 Elementary number-theoretic notions 927 31.2 Greatest common divisor 933 31.3 Modular arithmetic 939 31.4 Solving modular linear equations 946 31.5 The Chinese remainder theorem 950 31.6 Powers of an element 954 31.7 The RSA public-key cryptosystem 958 31.8 Primality testing 965 31.9 Integer factorization 975

### 32 String Matching 985

- T[n]: text
- P[m]: pattern (m <= n)
- s: shift - pattern P occurs beginning at position
*s+1*in text T- valid shift
- invalid shift

#### 32.1 The naive string-matching algorithm 988

O((n-m+1)*m)

#### 32.2 The Rabin-Karp algorithm 990

- 把字符串转换成一位一位的数字，用霍纳法则
- Θ(m) preprocessing, worst-case Θ((n-m+1)*m)
- used to detect plagiarism

#### 32.3 String matching with finite automata 995

#### 32.4 The Knuth-Morris-Pratt algorithm 1002

### 33 Computational Geometry 1014

33.1 Line-segment properties 1015 33.2 Determining whether any pair of segments intersects 1021 33.3 Finding the convex hull 1029 33.4 Finding the closest pair of points 1039

### 34 NP-Completeness 1048

- Problems which can be solved by polynomial-time algorithms are
**tractable/easy**. Otherwise,**intractable/hard** - Three classes of problems:
- P. solvable in polynomial time O(n^k)
- NP. verifiable (there exists a certifier) in polynomial time. (NP: nondeterministic polynomial-time)
- NPC. NP-complete
- If any NP-complete problem can be solved in polynomial time, then every problem in NP has a polynomial-time algorithm.
- Many CS scientists believe NP-complete problems are intractable.
- e.g. circuit-satisfiability problem

#### 34.1 Polynomial time 1053

#### 34.2 Polynomial-time verification 1061

- Certifiers and Certificates: Hamiltonian Cycle
**HAM-CYCLE**. Given an undirected graph G = (V, E), does there exist a simple cycle C that visits every node?**Certificate**. A permutation of the n nodes.**Certifier**. Check that the permutation contains each node in V exactly once, and that there is an edge between each pair of adjacent nodes in the permutation.**Conclusion**. HAM-CYCLE is in NP.

- P,NP,EXP
- P. Decision problems for which there is a poly-time algorithm.
- EXP. Decision problems for which there is an exponential-time algorithm.
- NP. Decision problems for which there is a poly-time certifier.

- P ⊆ NP, NP ⊆ EXP
**Does P = NP?****If yes**, Efficient algorithms for 3-COLOR, TSP, FACTOR, SAT…**If no**, No efficient algorithms for 3-COLOR, TSP, FACTOR, SAT…

#### 34.3 NP-completeness and reducibility 1067

34.4 NP-completeness proofs 1078 34.5 NP-complete problems 1086

### 35 Approximation Algorithms 1106

35.1 The vertex-cover problem 1108 35.2 The traveling-salesman problem 1111 35.3 The set-covering problem 1117 35.4 Randomization and linear programming 1123 35.5 The subset-sum problem 1128

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