Greedy algorithm
A greedy algorithm is an algorithm which, at each step, makes the choice that is locally optimal, and subsequently does not reconsider past choices.
At a glance
Key signals
0% cross fields · reaches 5 more
- Computer Science
- Algorithms
- Optimization
- Explanation
- Examples
- Misconception
- Sourced relations 2
- Attribution
Dependencies
What this concept builds on and what it makes possible — derived from the atlas’s dependency, causal and structural relations, not from every related edge.
System context
Greedy algorithmis aAlgorithmEstablished
greedy algorithm is a kind of Algorithm.
Mechanism: A greedy algorithm builds a solution by always taking the locally best choice, hoping the local best adds up to a good whole.
- Wikidata verifiedmoderate evidence
Structural role & consequence
Interpreted from the current atlas graph — what the connections mean, not just how many there are.
Currently dark in the atlas: no key date stored · 2 of 2 of its relations lack claim-level evidence.
atlas representation · Describes the current Thinking OS representation, not the state of the world.
Exercises 2 annotated mental models — a concept that connects several thinking patterns.
curated · Curated annotations, not a derived measure.
Structural neighbourhood: 2 → 16 → 81 concepts reachable within 3 hops.
structural · Structural reach — being reachable is not the same as being understood.
All 2 of its relationships stay within its own discipline — a field-specific concept in the current atlas.
structural · Structural graph analysis — not a claim of importance, causation or history.
cross-field
2 within-field, 0 cross-field
Strengths & constraints
Constraints
- Evidence coverage currently thin in the atlas — few of its relationships carry claim-level evidence. atlas representation
- No dated history stored — the atlas records no key date for this concept. atlas representation
Conditions
- Read structurally — most of its relationships carry no external evidence yet, so claims here are graph-derived. structural
Seen through each discipline
How this concept sits in each of its fields — derived from its real connections in the graph, not asserted.
Through this lens it connects to Algorithm and Dynamic programming.
Through this lens it connects to Algorithm and Dynamic programming.
Through this lens it connects to Dynamic programming.
Related ideas to explore
Concepts that look related but are not yet connected here — candidates for a connection to reason about, not established links.
This idea also appears in…
The same structure shows up in other disciplines. These are real recurrences drawn from the graph — a starting point for asking “what carries over, and what changes?”
Trade-offs41 disciplines · 31 concepts
Optimization14 disciplines · 10 concepts
Concepts
shares a mental model · shares a mental model · explicitly analogous
shares a mental model · shares a mental model
shares a mental model · shares a mental model
shares a mental model · shares a mental model
shares a mental model · shares a mental model
shares a mental model · shares a mental model
Mental models at work here
Trade-offs
When getting more of one thing means accepting less of another, because resources or constraints are limited.
Ask what you give up (the opportunity cost) to gain what you want.
Optimization
Searching a space of options for the best one under constraints — following a gradient of 'better' toward a maximum or minimum.
Ask: what is being maximised or minimised, and what limits the search? Beware settling in a 'local' best that isn't the global one.
- Connects 2 other ideas across 3 disciplines.
- A cross-disciplinary bridge — its connections reach into 5 other fields.
- Most of its connections are of the “Kind & structure” kind.
- It exercises 2 reusable thinking patterns.
Derived from the graph’s real structure — observations, not a score.
Sources
- Wikipedia (English & German editions) verifiedmoderate evidence
- Wikidata verifiedmoderate evidence