computer science

SMA*

SMA* 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 SMA* rather than just read about it. In short: SMA* or Simplified Memory Bounded A* is a shortest path algorithm based on the A* algorithm. The main advantage of SMA* is that it uses a bounded memory, while the A* algorithm might need exponential memory.

Key takeaways

SMA* 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 SMA* to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of SMA* from memory before moving on to harder problems.

Reference excerpt

SMA* or Simplified Memory Bounded A* is a shortest path algorithm based on the A* algorithm. The main advantage of SMA* is that it uses a bounded memory, while the A* algorithm might need exponential memory. All other characteristics of SMA* are inherited from A*.

Process

Properties SMA* has the following properties

It works with a heuristic, just as A* It is complete if the allowed memory is high enough to store the shallowest solution It is optimal if the allowed memory is high enough to store the shallowest optimal solution, otherwise it will return the best solution that fits in the allowed memory It avoids repeated states as long as the memory bound allows it It will use all memory available Enlarging the memory bound of the algorithm will only speed up the calculation When enough memory is available to contain the entire search tree, then calculation has an optimal speed

Implementation The implementation of Simple memory bounded A* is very similar to that of A*; the only difference is that nodes with the highest f-cost are pruned from the queue when there isn't any space left. Because those nodes are deleted, simple memory bounded A* has to remember the f-cost of the best forgotten child of the parent node. When it seems that all explored paths are worse than such a forgotten path, the path is regenerated. Pseudo code:

External links Simplified Memory Bounded A Star Search Algorithm | SMA* Search | Solved Example in by Mahesh Huddar

References

Worked examples

Example 1 — a first encounter with SMA*

Start with the simplest possible case. Write down what SMA* 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 SMA* 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 SMA* 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 SMA*

In research: SMA* 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 SMA* 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: SMA* is common in secondary-school and first-year university syllabi. It links to neighbouring topics Game artificial intelligence, Graph algorithms, Routing algorithms, so understanding it makes those chapters shorter.
In everyday life: Look for SMA* 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 SMA* in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what SMA* 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 SMA* out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is SMA* in simple terms?

Why does SMA* 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 SMA*?

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 SMA*.