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OI-style programming

OI-style programming. Gary Wong For any questions, please ask via Email: garywong612@gmail.com MSN: gary_wong612@hotmail.com. Before the training…. I never assume you knowing a lot It is okay to interrupt me whenever you think of any question to ask. Contents. OI-oriented skills

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OI-style programming

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  1. OI-style programming Gary Wong For any questions, please ask via Email: garywong612@gmail.com MSN: gary_wong612@hotmail.com

  2. Before the training… • I never assume you knowing a lot • It is okay to interrupt me whenever you think of any question to ask

  3. Contents • OI-oriented skills • Algorithms and data structures • Concept of “complexity”

  4. OI-oriented skills • Use one word to describe the aim of a contestant in a competition. WIN!

  5. OI-oriented skills • How to win? • Number of tasks attempted? • Using the most elegant solution? • Using the least time to “finish”? • Highest score! • Let us quickly review the scoring method used in OI competitions…

  6. OI-oriented skills • Scoring • A set of test data is fed into your program • Get the score when it outputs correctly • ALWAYS remember: judging systems never score you by reading the codes!

  7. OI-oriented skills • Recommended steps for solving problems in OI: 1. Reading the problems 2. Choosing a problem 3. Reading the problem 4. Thinking 5. Coding 6. Testing (and debugging) 7. Finalizing the program

  8. OI-oriented skills • Reading the problemS • Have a quick look • You should at least look at: • Length of problem statement • Input/Output format • Constraints • Range of variables • Time limit

  9. OI-oriented skills • Choosing a problem • Problem setters might not expect contestants to finish the whole paper • Usually from easy to difficult

  10. OI-oriented skills • Reading the problem • My own advice: read every single word! • Underline keywords if possible • NEVER make assumptions yourself • Ask if you are not sure

  11. OI-oriented skills • 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.

  12. OI-oriented skills • Thinking • Make sure you know what you are doing before coding • Points to note: • Expected running time of your program? • How much memory will be used? • Coding difficulties?

  13. OI-oriented skills • Coding • Short variable names • Usei, j, m, ninstead ofno_of_schools, name_of_students, etc. • No comments needed • As long as YOU understand YOUR code, okay to ignore all “appropriate“ coding practices • My opinion: poor coding style will eventually kill you once in your life

  14. OI-oriented skills • Coding • Edsger Wyber Dijkstra • A famous Dutch computer scientist • One of his great work is “Dijkstra’s algorithm” for finding shortest paths • He hates “spaghetti codes” a lot!

  15. OI-oriented skills • 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 ” • Always create your own test data • Manually • Using a program • Test for ALL possible cases (including tricky cases)

  16. OI-oriented skills • Testing • If time allows, cross check your “efficient” program with a “slower” program

  17. OI-oriented skills • Debugging • Easiest method: writeln/printf/cout • It is so-called “Debug message” • Use of debuggers: • FreePascal IDE debugger • gdb debugger

  18. OI-oriented skills • Finalizing • Make sure that the output format is EXACTLY the same as in the problem • Remember to delete all “debug messages” • Is your submitted code the most updated version? • Try to allocate ~5 mins at the end for finalizing

  19. 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

  20. Tricks • 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

  21. Common pitfalls • Even experienced contestants might have such problems! • Misunderstanding the problem • Not familiar with competition environment • Output format • Using complex algorithms unnecessarily • Choosing the hardest problem first • Too confident with himself/herself

  22. Contents • OI-oriented skills • Algorithms and data structures • Concept of “complexity”

  23. 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”

  24. 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” • E.g. “recursion” is NOT an algorithm

  25. Data structures • Briefly speaking, the way to organize data • Examples: • Binary Search Tree • Hash Table • Segment Tree • Different data structures have different properties • Efficiency • Amount of memory used • Different algorithms use different data structures

  26. Contents • OI-oriented skills • Algorithms and data structures • Concept of “complexity”

  27. 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

  28. 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 

  29. 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(n(n-1)/2)=O(n2/2–n/2)=O(n2) • Total space needed = size of array + space of variables • Space Complexity=32*n +32*3=O(n)+O(1)=O(n)

  30. 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 • In worst case, • number of iterations = lg n [lg means log2] • Time Complexity = O(lg n) • Total space needed = size of array + space of variables • Space Complexity = O(n)

  31. Complexity • What if… • An algorithm using bubble sort, followed by binary search? • O(f) + O(g) = max(O(f), O(g)) • Take the “maximum” one only, ignore the “smaller” one • Answer: O(n2)

  32. 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!

  33. Any question?

  34. Let’s know each other! 

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