algorithmic complexity

USAGE SUMMARY

The term 'algorithmic complexity' is correct and usable in written English.
You can use it to refer to the difficulty of solving problems with algorithms. For example, "The algorithmic complexity of challenging problems may require complex algorithms and data structures to solve."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

For the subclass of the PUP that belongs to the complexity class P we present a polynomial-time algorithm and give in-depth algorithmic complexity results.

Journal of Computer and System Sciences

The concept of algorithmic complexity defines a sequence by the shortest algorithm needed to generate that sequence.

FEMS Microbiology Reviews

Applying static pattern detection algorithms from scratch to every window is impractical due to their high algorithmic complexity and the real-time responsiveness required by streaming applications.

Information Systems

The algorithmic complexity of the suggested efficient ML decoding is.

EURASIP Journal on Wireless Communications and Networking

Moreover, the algorithmic complexity involved becomes prohibitive for large segments.

European Transport Research Review

The DFT algorithmic complexity is O(N 2).

EURASIP Journal on Advances in Signal Processing

Second, KS-entropy could be connected to algorithmic complexity (Brudno 1978).

SEP

The algorithmic complexity of SVM-SGD is linear with the number of datapoints.

Vietnam Journal of Computer Science

Performing ML detection using exhaustive search over all possible constellation points has algorithmic complexity of.

EURASIP Journal on Wireless Communications and Networking

These methods, however, bring with them the additional computational cost and algorithmic complexity.

EURASIP Journal on Advances in Signal Processing

Second, the algorithmic complexity of a sequence is the length of the shortest computer programme needed to reproduce the sequence.

SEP

Expert writing Tips

Best practice

When discussing algorithms, clearly specify whether you are referring to time complexity, space complexity, or other specific types of "algorithmic complexity" to avoid ambiguity.

Common error

Don't equate high "algorithmic complexity" with the difficulty of implementing an algorithm. An algorithm may have low complexity (e.g., linear time), but still be challenging to code correctly due to intricate logic or data structures.

Linguistic Context

The phrase "algorithmic complexity" functions as a noun phrase, typically used as a subject or object in a sentence. It refers to the measure of resources (such as time or space) required by an algorithm as a function of the input size. Ludwig AI confirms its correctness and usability.

Expression frequency: Very common

Frequent in

Science

95%

Academia

3%

Wiki

1%

Less common in

News & Media

1%

Formal & Business

0%

Reference

0%

Ludwig's WRAP-UP

In summary, "algorithmic complexity" is a well-established and frequently used term, particularly within scientific and academic fields, to quantify the resources required by algorithms. Ludwig AI confirms that this term is correct and usable in written English. Understanding its nuances, such as the distinction between time and space complexity, is crucial for effective communication and precise analysis in computer science. When writing, consider the specific type of complexity you're referring to and avoid conflating "algorithmic complexity" with the difficulty of implementation. By using this term accurately, you contribute to clear and insightful discussions about algorithm design and analysis.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

computational complexity

Replaces "algorithmic" with "computational", emphasizing the computational aspect rather than the algorithmic process itself. Often used interchangeably but may imply a broader scope.

time complexity

Focuses specifically on the time required to execute an algorithm, a key aspect of algorithmic complexity but not the only one.

space complexity

Focuses on the amount of memory space required by an algorithm, another critical aspect, but different from the overall complexity.

algorithm efficiency

Shifts the focus to the efficiency of the algorithm, which is inversely related to its complexity.

computational cost

Emphasizes the resources (time, memory) consumed by the computation, framing complexity as a cost.

program complexity

Broader term that encompasses the complexity of a program, which may include factors beyond just the algorithm.

code complexity

Focuses on the complexity of the code implementation of an algorithm, which can impact its overall complexity.

Kolmogorov complexity

A theoretical measure of algorithmic complexity based on the shortest possible program to generate a string.

descriptive complexity

Measures the complexity of describing a structure or pattern using a formal language.

minimum description length

A principle based on the idea that the best model for a given set of data is the one that achieves the greatest compression of the data; it's related to algorithmic complexity because compression implies finding a shorter "algorithm" to describe the data.

FAQs

How is "algorithmic complexity" measured?

Typically, "algorithmic complexity" is measured using Big O notation, which describes how the runtime or space requirements of an algorithm grow as the input size increases. Common examples include O(n), O(log n), and O(n^2).

What's the difference between "algorithmic complexity" and "computational complexity"?

While often used interchangeably, "algorithmic complexity" usually refers to the resources needed by a specific algorithm, while "computational complexity" refers to the inherent difficulty of a computational problem, regardless of the algorithm used.

Why is understanding "algorithmic complexity" important?

Understanding "algorithmic complexity" helps in choosing the most efficient algorithms for a given task, especially when dealing with large datasets, ensuring optimal performance and resource utilization.

What are some factors that affect "algorithmic complexity"?

Factors affecting "algorithmic complexity" include the size of the input data, the number of operations performed, and the data structures used. Optimizing these factors can lead to more efficient algorithms.