Category: c programming

~~~ 𝑻𝒉𝒆 π‘Ίπ’†π’“π’Šπ’†π’” 𝒐𝒇 π‘©π’Šπ’ˆ 𝑢 ~~~𝑷𝒂𝒓𝒕 – 𝑰𝑽

Polynomial time complexity in algorithms causes tasks to become increasingly complex with each additional input, much like managing a family WhatsApp group. Joining the group is O(n), deciphering messages is O(n^2), and managing disputes is O(n^3). Nested loops and multiple operations on the entire dataset indicate polynomial time complexity, such as O(n^2) or O(n^3).

Read More ~~~ 𝑻𝒉𝒆 π‘Ίπ’†π’“π’Šπ’†π’” 𝒐𝒇 π‘©π’Šπ’ˆ 𝑢 ~~~𝑷𝒂𝒓𝒕 – 𝑰𝑽

~~~ 𝑻𝒉𝒆 π‘Ίπ’†π’“π’Šπ’†π’” 𝒐𝒇 π‘©π’Šπ’ˆ 𝑢 ~~~𝑷𝒂𝒓𝒕 – 𝑰𝑰𝑰

In a linear time scenario, the time taken to complete a task grows with the size of the input. Like avoiding pesky questions at a big family gathering, each relative must be checked before finding the relative of interest. Similarly, in algorithms, checking each element in a list one by one creates a linear relationship. As datasets grow, efficient algorithms become crucial.

Read More ~~~ 𝑻𝒉𝒆 π‘Ίπ’†π’“π’Šπ’†π’” 𝒐𝒇 π‘©π’Šπ’ˆ 𝑢 ~~~𝑷𝒂𝒓𝒕 – 𝑰𝑰𝑰

~~~ 𝑻𝒉𝒆 π‘Ίπ’†π’“π’Šπ’†π’” 𝒐𝒇 π‘©π’Šπ’ˆ 𝑢 ~~~𝑷𝒂𝒓𝒕 – 𝑰𝑰

O(logn) indicates logarithmic time complexity, where the algorithm’s efficiency grows as the problem size reduces. For instance, in a sorted list search, you progressively eliminate half the list at each step, akin to finding a word in a dictionary by halving the search space. Various examples illustrate logarithmic time complexity, emphasizing its efficiency for large datasets.

Read More ~~~ 𝑻𝒉𝒆 π‘Ίπ’†π’“π’Šπ’†π’” 𝒐𝒇 π‘©π’Šπ’ˆ 𝑢 ~~~𝑷𝒂𝒓𝒕 – 𝑰𝑰

~~~ 𝑻𝒉𝒆 π‘Ίπ’†π’“π’Šπ’†π’” 𝒐𝒇 π‘©π’Šπ’ˆ 𝑢 ~~~𝑷𝒂𝒓𝒕 – 𝑰

Big O Notation provides a way to describe how the performance of an algorithm changes with input size, offering a worst-case scenario. O(1) represents constant time, where the algorithm takes the same amount of time regardless of input size. Examples include accessing array elements by index, swapping numbers, and inserting nodes in a linked list.

Read More ~~~ 𝑻𝒉𝒆 π‘Ίπ’†π’“π’Šπ’†π’” 𝒐𝒇 π‘©π’Šπ’ˆ 𝑢 ~~~𝑷𝒂𝒓𝒕 – 𝑰

𝑾𝒉𝒂𝒕 π’Šπ’” 𝒂 𝑹𝒂𝒄𝒆 π‘ͺπ’π’π’…π’Šπ’•π’Šπ’π’? 𝑫𝒐𝒆𝒔 π’Šπ’• π’“π’†π’‚π’π’π’š π’Žπ’‚π’•π’•π’†π’“?

A race condition in software occurs when multiple processes try to access shared resources simultaneously, leading to unpredictable outcomes. An example is the Therac-25 radiation therapy machine incidents in the 1980s, where a race condition in the machine’s software led to patients receiving massive overdoses of radiation, highlighting the critical need for thorough software testing and safety standards.

Read More 𝑾𝒉𝒂𝒕 π’Šπ’” 𝒂 𝑹𝒂𝒄𝒆 π‘ͺπ’π’π’…π’Šπ’•π’Šπ’π’? 𝑫𝒐𝒆𝒔 π’Šπ’• π’“π’†π’‚π’π’π’š π’Žπ’‚π’•π’•π’†π’“?

How is a floating-point number stored?

The computer allocates 32-bit memory for storing a float value, using IEEE 754 floating point conversion. With a positive value, the sign bit is 0. The integer and fraction are converted to binary, and the biased exponent is calculated and represented in the binary format. The final binary representation is 0 10000100 00001001001110110110010.

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Reading Declarations in C

When reading code, follow these points: 1) * – pointer to 2) [] – array of 3) () – function returning 4) Start at the variable name 5) Read right first without crossing parenthesis 6) Then go left without crossing 7) Start inwards and move outwards. Reading is right to left. Examples are provided for different variable types.

Read More Reading Declarations in C