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​Сложность алгоритмов. Big O. Основы

Сложность алгоритма — это количественная характеристика, которая говорит о том, сколько времени или какой объём памяти потребуется для выполнения алгоритма. Big O нотация используется для описания верхней границы.

В статье обсуждаются различные типы сложности, включая константную (O(1)), линейную (O(n)), логарифмическую (O(log n)), линеарифметическую (O(n * log n)) и квадратичную (O(n^2)).

Понимание Big O нотации критически важно для любого разработчика, так как это помогает оптимизировать код и делает его более эффективным.

Читаем тут: bimlibik.github.io/posts/complexity-of-algorithms/

#алгоритмы

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​Сложность алгоритмов. Big O. Основы

Сложность алгоритма — это количественная характеристика, которая говорит о том, сколько времени или какой объём памяти потребуется для выполнения алгоритма. Big O нотация используется для описания верхней границы.

В статье обсуждаются различные типы сложности, включая константную (O(1)), линейную (O(n)), логарифмическую (O(log n)), линеарифметическую (O(n * log n)) и квадратичную (O(n^2)).

Понимание Big O нотации критически важно для любого разработчика, так как это помогает оптимизировать код и делает его более эффективным.

Читаем тут: bimlibik.github.io/posts/complexity-of-algorithms/

#алгоритмы
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​Сложность алгоритмов. Big O. Основы

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