# What is space time trade off of algorithm?

## What is space time trade off of algorithm?

A space–time Trade off or time–memory trade-off in computer science is a case where an algorithm or program trades increased space usage with decreased time.

## What is the time space tradeoff principle give an example?

A classic example of a space/time tradeoff is the lookup table. A lookup table pre-stores the value of a function that would otherwise be computed each time it is needed. For example, 12! is the greatest value for the factorial function that can be stored in a 32-bit int variable.

**What are the examples of time space trade off?**

Types of Space-Time Trade-off

- Compressed or Uncompressed data.
- Re Rendering or Stored images.
- Smaller code or loop unrolling.
- Lookup tables or Recalculation.

### What is the importance of space and time trade tradeoffs?

Space-time trade-offs are prevalent in biology, cryptography and dynamic programming. If your problem is taking a long time but not much memory, a space time trade-off would let you use more memory and solve the problem more quickly. Larger code size can be used to increase program speed when using loop unwinding.

### What do you mean by trade-off?

Definition of trade-off 1 : a balancing of factors all of which are not attainable at the same time the education versus experience trade-off which governs personnel practices— H. S. White. 2 : a giving up of one thing in return for another : exchange. Other Words from trade-off Synonyms Learn More About trade-off.

**Which is more important space or time complexity?**

Time complexity is often actually less important than space complexity, though obviously both matter. Sometimes time complexity matters more however. Your space is fixed for any set of hardware. If you don’t have enough, you just can’t run the algorithm.

## What is an algorithm write the characteristics of an algorithm?

Characteristics of an Algorithm Input − An algorithm should have 0 or more well-defined inputs. Output − An algorithm should have 1 or more well-defined outputs, and should match the desired output. Finiteness − Algorithms must terminate after a finite number of steps.

## What is the time and space complexity?

Time complexity is a function describing the amount of time an algorithm takes in terms of the amount of input to the algorithm. Space complexity is a function describing the amount of memory (space) an algorithm takes in terms of the amount of input to the algorithm.

**What is the significance of time and space complexity of an algorithm?**

Time complexity of an algorithm quantifies the amount of time taken by an algorithm to run as a function of the length of the input. Similarly, Space complexity of an algorithm quantifies the amount of space or memory taken by an algorithm to run as a function of the length of the input.

### What is time-space trade-off in algorithms?

In this article, we will discuss Time-Space Trade-Off in Algorithms. A tradeoff is a situation where one thing increases and another thing decreases. It is a way to solve a problem in: Either in less time and by using more space, or

### What is a tradeoff in Computer Science?

A tradeoff is a situation where one thing increases and another thing decreases. It is a way to solve a problem in: In very little space by spending a long amount of time. The best Algorithm is that which helps to solve a problem that requires less space in memory and also takes less time to generate the output.

**What are the different types of space-time trade-off?**

Types of Space-Time Trade-off 1 Compressed or Uncompressed data 2 Re Rendering or Stored images 3 Smaller code or loop unrolling 4 Lookup tables or Recalculation

## What is the Big-O analysis for algorithms?

For any algorithm, the Big-O analysis should be straightforward as long as we correctly identify the operations that are dependent on n, the input size. In general cases, we mainly used to measure and compare the worst-case theoretical running time complexities of algorithms for the performance analysis.