Introduction to HKOI

1. Introduction to HKOI Gary Wong

2. Ice Breaking and bond forming…

3. Rules • Level 1 • Form a big circle • The person holding the deck of cards will start the game, by introducing himself, and then passes the deck of cards to his left. • In each preceding turn, the person holding the deck of cards will repeat what the previous person has said, and then introduces himself. After that, he will passes the deck to his left. • The game ends when the deck of cards return to the first person.

4. Rules • Level 2 • Form a big circle • The person holding the deck of cards will start the game, by introducing himself and drawing a card from the deck. After that, he will pass the deck of cards to the kth person on his left, where k is the number written on the card he draw. • In each preceding turn, the person holding the deck of cards will repeat what the previous person has said, and then introduces himself. After that, he will draw a card from the deck and pass the deck of cards to the kth person on his left, where k is the number written on the card he draw. • The game ends when the deck runs out of cards.

5. Why OI? • Get medals? • Love solving problems? • Learn more? • Make friends? • … • OI could be a thing to give you all these

6. Agenda • Algorithms, Data Structures • Complexity • OI Style Programming • Training Sessions • Upcoming Challenges

7. Algorithms, Data Structures the best couple…

8. Algorithms • “Informally, an algorithm is any well-defined computational procedure that takes some value, or set of values, as input and produces some value, or set of values, as output. An algorithm is thus a sequence of computational steps that transform the input into the output.” [CLRS] • N.B.: CLRS = a book called “Introduction to algorithms”

9. Algorithms • In other words, a series of procedures to solve a problem • Example: • Bubble Sort, Merge Sort, Quick Sort • Dijkstra’s Algorithm, Bellman Ford’s Algorithm • Common misconceptions: • Algorithm = Program • Confusion between “algorithms” and “methods to design algorithms”

10. Data Structures • Briefly speaking, the way to organize data • Examples: • Binary Search Tree • Hash Table • Segment Tree • Different data structures have different properties • Different algorithms use different data structures

11. Don’t worry! • All the above-mentioned technical jargons will be taught later  • So, come to attend training! 

12. Complexity a performance indicator…

13. Complexity • We want to know how well an algorithm “scales” in terms of amount of data • In BOTH time and space • Only consider the proportionality to number of basic operations performed • A reasonable implementation can pass • Minor improvements usually cannot help

15. Complexity • Big-O notation • Definition We say that f(x) is in O(g(x)) if and only if there exist numbers x0 and M such that |f(x)| ≤ M |g(x)| for x > x0 • You do not need to know this 

16. Complexity • Example: Bubble Sort • For i := 1 to n do For j := 2 to i do if a[j] > a[j-1] then swap(a[j], a[j-1]); • Worst case number of swaps = n(n-1)/2 • Time Complexity = O(n2) • Total space needed = size of array + space of variables • Space Complexity = 32*n +32*3 = O(n) +O(1) = O(n)

17. Complexity • Another example: Binary search • While a<=b do m=(a+b)/2 If a[m]=key, Then return m If a[m]<key, Then a=m+1 If a[m]>key, Then b=m-1 • Worst case number of iterations = lg n [lg means log2] • Time Complexity = O(log n) • Total space needed = size of array + space of variables • Space Complexity = O(n)

18. What if… • An algorithm involving both bubble sort and binary search? • O(f) + O(g) = max(O(f), O(g)) • Take the “maximum” one only, ignore the “smaller” one • Answer: O(n2)

19. Complexity • Points to note: • Speed of algorithm is machine-dependent • Use suitable algorithms to solve problems • E.g., if n=1000 and runtime limit is 1s, would you use: • O(n2)? • O(n!)? • O(n3)? • Constant hidden by Big-O notation • Testing is required!

20. OI-Style Programming from abstract theory to (dirty) tricks…

21. OI-Style Programming • Objective of Competition… • The winner is determined by: • Fastest Program? • Amount of time used in coding? • Number of Tasks Solved? • Use of the most difficult algorithm? • Highest Score • Rule of thumb: ALWAYS aim to get as many scores as you can

22. OI-Style Programming • Scoring: • A “black box” judging system • Test data is fed into the program • Output is checked for correctness • No source code is manually inspected • How to take advantage (without cheating of course!) of the system?

23. OI-Style Programming • Steps for solving problems in OI: 1. Reading the problems 2. Choosing a problem 3. Reading the problem 4. Thinking 5. Coding 6. Testing 7. Finalizing the program

24. Reading the problems • Problems in OI: • Title • Problem Description • Constraints • Input/Output Specification • Sample Input/Output • Scoring

25. Reading the problems • Constraints • Range of variables • Execution Time • NEVER make assumptions yourself • Ask whenever you are not sure • (Do not be afraid to ask questions!) • Read every word carefully • Make sure you understand before going on

26. Thinking • Classify the problem into certain type(s) • Rough works • Special cases, boundary cases • No idea? Give up first, do it later. Spend time for other problems.

27. Thinking • Make sure you know what you are doing before coding • Points to note: • Complexity (BOTH time and space) • Coding difficulties • What is the rule of thumb mentioned?

28. Coding • Short variable names • Use i, j, m, n instead of no_of_schools, name_of_students, etc. • No comments needed • As long as YOU understand YOUR code, okay to ignore all “appropriate“ coding practices • NEVER use 16 bit integers (unless memory is limited) • 16 bit integer may be slower! (PC’s are usually 32-bit, even 64 bit architectures should be somewhat-optimized for 32 bit)

29. Coding • Usegoto, break, etc in the appropriate situations • Never mind what Dijkstra has to say  • Avoid using floating point variables if possible (eg. real, double, etc) • Do not do small (aka useless) “optimizations” to your code • Save and compile frequently

30. Testing • Sample Input/Output“A problem has sample output for two reasons: • To make you understand what the correct output format is • To make you believe that your incorrect solution has solved the problem correctly ” • Manual Test Data • Program-generated Test Data (if time allows) • Boundary Cases (0, 1, other smallest cases) • Large Cases (to check for TLE, overflows, etc) • Tricky Cases • Test by self-written program (again, if time allows)

31. Debugging • Debugging – find out the bug, and remove it • Easiest method: writeln/printf/cout • It is so-called “Debug message” • Use of debuggers: • FreePascal IDE debugger • gdb debugger

32. Finalizing • Check output format • Any trailing spaces? Missing end-of-lines? (for printf users, this is quite common) • better test once more with sample output • Remember to clear those debug messages • Check I/O – filename? stdio? • Check exe/source file name • Is the executable updated? • Method of submission? • Try to allocate ~5 mins at the end of competition for finalizing

33. OI-Style Programming • 2nd time to ask: What is the rule of thumb? • Tricks might be needed (Without violating rules, of course)

34. Tricks • Solve for simple cases • 50% (e.g. slower solution, brute force) • Special cases (smallest, largest, etc) • Incorrect greedy algorithms • Very often, slow and correct solutions get higher scores than fast but wrong solutions • Hard Code • “No solution” • Stupid Hardcode: begin writeln(random(100)); end. • Naïve hardcode: “if input is x, output hc(x)” • More “intelligent” hardcode (sometimes not possible): pre-compute the values, and only save some of them

35. Pitfalls • Misunderstanding the problem • Not familiar with competition environment • Output format • Using complex algorithms unnecessarily • Choosing the hardest problem first

36. Training Sessions a moment for inspiration…

37. Training Sessions • Intermediate and Advanced • ALL topics are open to ALL trainees • Tips: Pre-requisites are often needed for advanced topics

38. Training Sessions • On Saturday • Room 123, Ho Sin-Hang Engineering Building, Chinese University of Hong Kong • AM session: 10:00-12:30 • Lunch • PM session: 13:30-16:00 • http://www.hkoi.org for more details, including latest training schedule and notes

39. Training Sessions • A gross overview of topics covered: • Algorithms and Data Structures • Linux • Free, popular and powerful • Competition environment • C++ • Advantage of Stardard Template Library (STL)

40. Upcoming Challenges go for it!!!

41. Upcoming Challenges • Asia-Pacific Informatics Olympiad (7 May 2011) • Team Formation Test / TFT (28 May 2011) • Provided that you can get through TFT, • International Olympiad in Informatics • National Olympiad in Informatics • ACM Local

42. Upcoming Challenges • How can I prepare for these challenges? • Attend trainings • Participate into mini-competitions • Search for learning materials in Internet • Read books • Practice, practice, practice • PERFECT practice makes perfect • HKOI Online Judge: http://judge.hkoi.org • Other online judges (UVa, POJ, etc.)

43. Hard sell… • Intermediate Topic: “Searching and Sorting” (10:00-12:30, 22 Jan 2011) by Gary Wong 

44. Thank you for your tolerance =P

45. Reference • PowerPoint for HKOI 2010 Training Session 1 • “Introduction to HKOI” • “Algorithms, OI Style Programming”