computer science

Learning augmented algorithm

Learning augmented algorithm is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Learning augmented algorithm rather than just read about it. In short: A learning augmented algorithm (also called algorithm with predictions) is an algorithm that can make use of a prediction to improve its performance. Whereas in regular algorithms just the problem instance is inputted, learning augmented algorithms accept an extra parameter.

Key takeaways

Learning augmented algorithm belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Learning augmented algorithm to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Learning augmented algorithm from memory before moving on to harder problems.

Reference excerpt

A learning augmented algorithm (also called algorithm with predictions) is an algorithm that can make use of a prediction to improve its performance. Whereas in regular algorithms just the problem instance is inputted, learning augmented algorithms accept an extra parameter. This extra parameter often is a prediction of some property of the solution. This prediction is then used by the algorithm to improve its running time or the quality of its output. The most common application are online algorithms, where a prediction on the uncertain instance is provided.

Description A learning augmented algorithm typically takes an input ( I , A ) {\displaystyle ({\mathcal {I}},{\mathcal {A}})} . Here I {\displaystyle {\mathcal {I}}} is a problem instance and A {\displaystyle {\mathcal {A}}} is the prediction. A prediction can be any object. Common are the following types:

Prediction of an optimal solution. The prediction gives a solution to the problem or characterizes an optimal solution. Prediction of the input. This is mainly used for online problems. Prediction of algorithmic actions. A prediction tailored to a specific algorithm that suggests a specific algorithm execution. Learning augmented algorithms usually satisfy the following three properties:

Consistency. A learning augmented algorithm is said to be consistent if the algorithm can be proven to have a good performance when it is provided with an accurate prediction. Smoothness. A learning augmented algorithm is called smooth if its performance can be bounded by a function of the quality of the prediction. Here, the quality can be measured in a problem specific way. This is also called the prediction error. Robustness. A learning augmented algorithm is called robust if its worst-case performance can be bounded even if the given prediction is inaccurate. Learning augmented algorithms generally do not prescribe how the prediction should be done. For this purpose machine learning can be used.

Applications A few examples of problems where learning augmented algorithms have been applied are the following.

Online algorithms The ski rental problem The weighted paging problem The set cover problem Nonclairvoyant scheduling The online bipartite matching problem

Warm starting

Data structures The binary search algorithm is an algorithm for finding elements of a sorted list x 1 , … , x n {\displaystyle x_{1},\ldots ,x_{n}} . It needs O ( log ⁡ ( n ) ) {\displaystyle O(\log(n))} steps to find an element with some known value y {\displaystyle y} in a list of length n {\displaystyle n} . With a prediction i {\displaystyle i} for the position of y {\displaystyle y} , the following learning augmented algorithm can be used.

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Learning augmented algorithm

Start with the simplest possible case. Write down what Learning augmented algorithm claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Learning augmented algorithm before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Learning augmented algorithm ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Learning augmented algorithm

In research: Learning augmented algorithm appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Learning augmented algorithm in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Learning augmented algorithm is common in secondary-school and first-year university syllabi. It links to neighbouring topics Algorithms, Theoretical computer science, so understanding it makes those chapters shorter.
In everyday life: Look for Learning augmented algorithm outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Learning augmented algorithm in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Learning augmented algorithm means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Learning augmented algorithm out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Learning augmented algorithm in simple terms?

Why does Learning augmented algorithm matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Learning augmented algorithm?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Learning augmented algorithm.