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Reverse Bits

Bit Manipulation
O(log n) time, O(1) space
Intermediate

Reverse the bit pattern of a 32-bit unsigned integer efficiently using bit manipulation. For example, 43261596 (binary: 00000010100101000001111010011100) becomes 964176192 (binary: 00111001011110000010100101000000) after reversal. This operation is critical in Fast Fourier Transform (FFT) bit-reversal permutation, graphics processing, cryptographic operations, and understanding little-endian/big-endian conversions at the bit level.

Prerequisites:
Bitwise shift operators
Binary representation
Unsigned integers

Visualization

Interactive visualization for Reverse Bits

Interactive visualization with step-by-step execution

Implementation

Language:
1function reverseBits(n: number): number {
2  let result = 0;
3  for (let i = 0; i < 32; i++) {
4    result = (result << 1) | (n & 1);
5    n >>= 1;
6  }
7  return result >>> 0; // Convert to unsigned
8}

Deep Dive

Theoretical Foundation

Bit reversal mirrors the bit pattern of an integer. Iterative approach: process each of 32 bits, extracting rightmost bit from input and building result from right-to-left by left-shifting result and ORing with extracted bit. Time: O(32) = O(1) for fixed width. Divide-and-conquer optimization: recursively swap halves (16 bits), then quarters (8 bits), then 4-bit chunks, 2-bit pairs, finally adjacent bits - this gives O(log log n) = O(log 32) = O(5) operations. Lookup table method: precompute reversal for bytes, process 4 bytes separately. Choice depends on use case: iterative is simple, divide-conquer is faster for many calls, lookup uses more memory.

Complexity

Time

Best

O(log n)

Average

O(log n)

Worst

O(log n)

Space

Required

O(1)

Applications

Industry Use

1

Fast Fourier Transform (FFT) bit-reversal permutation

2

Graphics rendering (texture mapping)

3

Cryptographic operations

4

Network protocol implementation

5

Digital signal processing

6

Binary tree level-order to in-order conversion

7

Endianness conversion

8

Reed-Solomon error correction

Use Cases

Cryptography
Graphics
Network protocols

Related Algorithms

Count Set Bits (Brian Kernighan's Algorithm)

Efficiently count the number of 1 bits (set bits) in the binary representation of an integer. Brian Kernighan's algorithm is one of the most elegant bit manipulation techniques, invented by Brian Kernighan (co-author of 'The C Programming Language'). The algorithm repeatedly clears the rightmost set bit, making it optimal as it only loops for the number of set bits rather than all bits.

Bit Manipulation

Check if Power of Two

Determine if a given integer is a power of 2 using a brilliant single bitwise operation. Powers of 2 in binary have exactly one set bit: 1=0001, 2=0010, 4=0100, 8=1000, 16=10000. This unique property enables an O(1) constant-time check using the elegant formula: n > 0 && (n & (n-1)) == 0. This trick is widely used in systems programming, memory management, and optimization.

Bit Manipulation

XOR Operations Collection

Comprehensive collection of XOR (exclusive OR) bit manipulation techniques with unique mathematical properties: a ⊕ a = 0 (self-inverse), a ⊕ 0 = a (identity), commutative, and associative. XOR is fundamental in finding unique/missing elements, in-place swapping, parity checking, cryptography, error detection, and many optimization problems. These elegant properties make XOR one of the most powerful bitwise operators.

Bit Manipulation

Add Two Numbers (Bitwise)

Perform integer addition using only bitwise operations (XOR, AND, shift) without the + operator, mimicking how CPUs add numbers at the hardware level using logic gates. This algorithm decomposes addition into sum-without-carry (XOR) and carry-generation (AND + shift), iterating until no carry remains. Fundamental for understanding computer architecture, ALU design, and implementing arithmetic when + operator is unavailable or expensive.

Bit Manipulation
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