Rogério Alves Capa SS FS Crooded - Big Oh Londrina-Pr Brazil! Photo: @criadaruaproducoes #koffeeskate #koffee #skateboard #skate #streetskate #skatederua #hardware #parafusosdebase #londrina #parana #brazil #bigoh #criadarua #rogerioalvescapa #capa
seen from Yemen
seen from Brazil
seen from Malaysia

seen from United Kingdom
seen from Brazil

seen from United States

seen from France
seen from China

seen from Thailand

seen from United States
seen from Germany

seen from United States

seen from United Kingdom

seen from United States

seen from Italy
seen from Singapore

seen from United States
seen from United Kingdom
seen from United States
seen from United States
Rogério Alves Capa SS FS Crooded - Big Oh Londrina-Pr Brazil! Photo: @criadaruaproducoes #koffeeskate #koffee #skateboard #skate #streetskate #skatederua #hardware #parafusosdebase #londrina #parana #brazil #bigoh #criadarua #rogerioalvescapa #capa
Photo: Diego Rett Over Crooked Big Oh Matinals! #skateboard #skate #streetskate #skatederua #bigoh #bigohmatinals #worshipskateshop #ninecloudsskateboard #overjoyskateboard #diegorett #rogerioalvescapa #capa
Big Oh Matinals! #skateboard #skate #streetskate #skatederua #bigohmatinals #bigoh #thiagomilan #brendonwilliam #rogerioalvescapa #capa
Big Oh Matinals! #skateboard #skate #streetskate #skatederua #bigoh #bigohmatinals #hurikfranco #pablosilva #rogerioalvescapa #capa
Big Oh! (Week 11)
Hey! This week we looked further into Big Oh and run times.
Last week, we defined Big Oh as:
t e O(g) if there are positive constants c and B so that for every natural number n no smaller than B, t(n) <= cg(n)
Using the Big Oh theorem, we can derive that if t e O(n), then t e O(n^2). This then can be extrapolated to show that O(1) is a subset of O(lg(n), which is a subset of O(n), which is a subset of O(n^2), which is a subset of O(n^3), and so on.
Big Oh is a way of showing us run time. If a function’s run time is in O(n), we can assume it will take n steps. If it’s run time is in O(lg(n)), we can assume it will take lg(n) steps. An example of code run time (in iteration, in this case) can be looked at as follows:
The code above has a run time in O(n^5). This is because the first while loop will run in n^3 steps, and the second will run in n^2 steps, however, when nested as shown, the n^2 step loop will run n^3 times, meaning it will run n^2 x n^3 times, that being n^5.
A summary of the Big Oh theorem with respect to different statements in code is as follows:
# sequences: max(O(each statement))
# loops: product of iteration of each loop (what we looked at above)
# conditions: worst possible case.
Diego ReTT @diegorett marretando em seu habitat natural #BigOh #nineclouds #amteam #realskateco #fuckallposers
Tech Hangout #23 – Introduction to Algorithms: Computational Complexity
В эту среду, 24 апреля, Василий Наквасюк начал серию коротких докладов про алгоритмы, сложность алгоритмов, структуры данных, виды алгоритмов (сортировка, ...) и т.п. В первом докладе постарался быстро рассказать про вычислительную сложность алгоритмов и нотацию "О-большое" на примерах.
Смотреть презентацию
Смотреть видео (совсем скоро!)