Thinking·OS

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Algorithm models Turing machine. Activate to inspect this relation.Algorithm is a Sequence. Activate to inspect this relation.Analysis of algorithms is a Computational complexity theory. Activate to inspect this relation.Big O notation is part of Analysis of algorithms. Activate to inspect this relation.BQP (quantum complexity) is part of Computational complexity theory. Activate to inspect this relation.Class P is part of Computational complexity theory. Activate to inspect this relation.Computational biology applies to Algorithm. Activate to inspect this relation.Computational complexity theory is a Computability theory. Activate to inspect this relation.Computational complexity theory is part of Algorithm. Activate to inspect this relation.Data compression depends on Algorithm. Activate to inspect this relation.DNA is analogous to Algorithm. Activate to inspect this relation.Greedy algorithm is a Algorithm. Activate to inspect this relation.Iterative method is a Algorithm. Activate to inspect this relation.Machine learning is a Algorithm. Activate to inspect this relation.NP-complete applies to Optimization. Activate to inspect this relation.NP-complete is part of Computational complexity theory. Activate to inspect this relation.NP-completeness is part of Computational complexity theory. Activate to inspect this relation.P versus NP applies to Algorithm. Activate to inspect this relation.Randomized algorithm is a Algorithm. Activate to inspect this relation.Search algorithm is a Algorithm. Activate to inspect this relation.Sorting algorithm is a Algorithm. Activate to inspect this relation.Turing machine models Algorithm. Activate to inspect this relation.NP-completeOptimizationComputational complexity theoryComputability theoryAnalysis of algorithmsAlgorithmBQP (quantum complexity)Class PNP-completenessBig O notationSequenceMachine learningGreedy algorithmSearch algorithmData compressionDNASorting algorithmTuring machineComputational biologyIterative methodP versus NPRandomized algorithm
Relationship types

9 concepts viewed through this lens. Bridge concepts connect this view to Artificial Intelligence, Biochemistry, Bioinformatics, Biology….

Legend
  • Focused concept
  • Connected concept
  • Bridge concept (just outside the lens)
  • Arrow points from cause / source to effect / target
  • A line with no arrow is a two-way relationship
  • Node colour marks the concept’s primary discipline
22 concepts22 relationships29 disciplines5 relation families

NP-complete

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At a glance

In computational complexity theory, NP-complete problems are the hardest of the problems to which solutions can be verified quickly.

Disciplines
Computer Science · Algorithms
Role in the graph
Cross-disciplinary bridge — reaches Business, Economics, Engineering, Mathematical Modelling, Optimization, Systems Engineering
Relationships
2 · 2 relation families

What am I looking at?

In this lens (9)

Bridge concepts (19)

Just outside the lens — they connect it to other context.

Insights from this view

Structural observations about the concepts shown here — descriptions of this graph, not claims about the world.

  • This view connects 29 disciplines: Algorithms, Artificial Intelligence, Biochemistry, Bioinformatics, Biology, Business, Cell Biology, Computational Biology, Computational Complexity, Computer Science, Data Structures, Discrete Mathematics, Economics, Engineering, Evolutionary Biology, Genetics, History, Information Theory, Logic, Machine Learning, Mathematical Modelling, Mathematics, Molecular Biology, Numerical Analysis, Optimization, Software Engineering, Statistics, Systems Engineering, Theory of Computation.
  • NP-complete is a bridge concept — viewed here through Algorithms, Computer Science.
  • The connections here span 5 relation families.
  • Information explains 6 concepts in this view (Algorithms, Artificial Intelligence, Biochemistry, Bioinformatics, Cell Biology, Computational Complexity, Computer Science, Data Structures, Discrete Mathematics, Evolutionary Biology, Genetics, History, Information Theory, Logic, Machine Learning, Mathematics, Molecular Biology, Software Engineering, Statistics, Theory of Computation).

Relationships as a list

The focused concept’s relationships. Pick another concept in the graph above to update this list.

Explore through a different lens

A lens is a deterministic projection of the graph. Pick a discipline, thinking pattern or journey to reframe the whole view.

By discipline

ChemistryBiologyPhysicsMathematicsComputer ScienceGeologyMineralogyEconomics

By thinking pattern

Bayesian updatingCause and effectCompoundingConservationConstraintsDefense in depthDiffusion & spreadEmergenceEnergy transformationEntropy & irreversibilityEquilibriumFeedback loopIncentivesInformationLevels & hierarchyModularityNetworksOptimizationOscillationProbabilityRecursion & self-similarityScaleSignal vs noiseSource–pathway–receptorStocks and flowsSymmetry & invarianceThresholds & tipping pointsTrade-offsTriangulationVariation & selectionWaves

By journey

From a Leaf to the ClimateWhy Music Moves UsHow Machines LearnReasoning about a Leaf and the Climate

Concept collections

Concept collections are curated lenses onto the fabric — themed sets of ideas that recur across disciplines. They are not journeys; they are a way to read the graph.

About this view

What this is

Start from one concept and expand outward. The view never shows everything at once — click a node to refocus, filter by relationship type, or switch to an accessible list.

One fabric

3750 concepts and 5051 typed relations form one connected component — no isolated silo.

How to read it

Focus a concept, or apply a lens (discipline, mental model, journey) to see only the threads that matter.