StudioEgo's Thoughts, seasonⅡ

국제교류센터에서는 하계방학 중 일본의 자매대학에 파견할 단기일본어학연수단 참가희망자를 다음과 같이 모집하니 많은 지원 바랍니다.

1. 모집내용
가. 연수기관 : 교토조형예술대학 (일본 교토 소재)
나. 연수기간 : 2006. 7. 13(목) ~ 2006. 7. 22(토) : 9박10일간
다. 선발인원 : 20 ~ 25명
라. 연수비용 : 총 135만원 중 90만원 교비지원(본인부담액 45만원)

1. FireFox
Active-X쓰는 홈페이지에 들어가는것 빼곤 거의 FireFox로 인터넷을 돌아다닌다. 탭브라우징 기능과 수 많은 플러그인이 있고 가벼워서 IE보다 좋다고 생각한다.
2. EditPlus
PHP나 JAVA, C, C++등등의 언어들을 편집할때 많이 쓴다. 엄청나게 강력한 에디트이다. FTP에 들어가서 파일 불러들어와 코딩하고 저장하는 기능이 너무 좋아서 이 에디트를 돈주고까지 샀다. 돈주고 산것이 너무 아깝다고 생각이 안들 정도인데 그 돈도 너무 싸다고 생각든다. 국산이라는 점이 마음에 든다. :-)
3. iPuTTY
터미널 프로그램중에서 괜찮은 것 같다. 예전에는 zterm이라는 단순한 터미널 프로그램을 썼었는데 iPuPPY는 기능이 많은 것 같다. 써 본지는 얼마 되지 않는다.
4. Visual C++ 6.0
Win32 API로 프로그램짤때와 MFC프로그램 짤때 많이 쓴다. 1학년때에는 C, C++코딩을 다 여기서 했는데 2학년 되니 거의 Linux환경에서 GCC로 코딩을 하니 사용 빈도수가 엄청나게 줄어버린 것 같다.
5. Java 2 Platform Standard Edition 5.0
JAVA공부하기 전에 꼭 깔아야 한다.
6. GOM Player
괜찮은 플레이어이다. 코덱 안깔아도 되니 좋다.
7. 빵집
이건 alz파일, iso파일도 인식을 한다. 국산 압축프로그램이다. 알집같이 복잡한 라이센스를 안걸어넣은 점이 좋다.
8. Picasa
사진 정리할때 많이 쓰는 프로그램이다. Google에서 많이 퍼뜨린 프로그램이다. 사진들을 잘 찾아준다.
9. Google Desktop
컴퓨터속 파일들을 너무나 잘 찾아준다. 설마 해서 이상한 단어 집어 넣어더니 너무나 검색이 잘되어서 깜짝놀라버렸다. =_=
10. MSN Messenger
친구들과 의사소통할때는 거의 MSN Messenger로 한다. 아님 Google Talk도 있지만 10개를 꽉 채우는 바람에 집어 넣지 못했다-_-;;

Graph
In computer science, a graph is an abstract data type (ADT) that consists of a set of nodes and a set of edges that establish relationships (connections) between the nodes. The graph ADT follows directly from the graph concept from mathematics.

A graph G is defined as follows: G=(V,E), where V is a finite, non-empty set of vertices and E is a set of edges (links between pairs of vertices). When the edges in a graph have no direction, the graph is called undirected, otherwise called directed. In practice, some information is associated with each node and edge.

Representation

---

In typical graph implementations, nodes are implemented as structures or objects. There are several ways to represent edges, each with advantages and disadvantages:

As an adjacency list
An adjacency list associates each node with an array of incident edges. If no information is required to be stored in edges, only in nodes, these arrays can simply be pointers to other nodes and thus represent edges with little memory requirement. An advantage of this approach is that new nodes can be added to the graph easily, and they can be connected with existing nodes simply by adding elements to the appropriate arrays. A disadvantage is that determining whether an edge exists between two nodes requires O(n) time, where n is the average number of incident edges per node.

As an adjacency matrix
An alternative way is to keep a square matrix (a two-dimensional array) M of boolean values (or integer values, if the edges also have weights or costs associated with them). The entry Mi,j then specifies whether an edge exists that goes from node i to node j. An advantage of this approach is that finding out whether an edge exists between two nodes becomes a trivial constant-time memory look-up. Similarly, adding or removing an edge is a constant-time memory access. The shortest path between any two nodes can be determined using the Floyd-Warshall algorithm. A disadvantage is that adding or removing nodes from the graph requires re-arranging the matrix accordingly, which may be costly depending on its size.

Other representations
Yet another way is based on keeping a relation (table) of edges, with key (source, target), where source and target are the connected vertices. Known algorithms allow the table to be manipulated and searched in loglinear time. Mneson [1] takes this approach.

In the general case, a graph may consist of many edges between many vertices, and unless the matrix representation for the edges is chosen, there may even be more than one edge connecting the same pair of vertices. Edges can be bidirectional or unidirectional.

Most data structures that are graphs are more structured than the general graph. A graph may, for example, be acyclic. In this case, each edge is unidirectional, and there is no way to traverse the edges in such a way as to ever visit the same node twice. An example of an acyclic graph is a directed acyclic word graph, a method of encoding a word-list for computer versions of word games such as Scrabble. A simple example of an acyclic graph is a non-circular singly linked list.

In most cases, the only information contained by the edge is that there is a relationship between the two nodes connected, and the information is stored in the node itself. However, some graphs have numerical values associated with each edge. These graphs can be used for different problems such as the Traveling salesman problem.

Additionally, there are graph-like structures where information is kept in the edges. One data structure has all of the information in the edges, and none of the information is in the nodes. This data structure can be very useful in modelling things like the pipes in a factory, or the wires in an airplane.

Operations

---

Graph algorithms are a significant field of interest for computer scientists. Typical operations associated with graphs are: finding a path between two nodes, like depth-first search and breadth-first search and finding the shortest path from one node to another, like Dijkstra's algorithm.