nested for loops

as donto believed on

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)

nested for loops has 36 facts recorded in Dontopedia across 13 references, with 5 live disagreements.

36 facts·17 predicates·13 sources·5 in dispute

Mostly:rdf:type(10), outer loop(5), inner loop(4)

Rdf:typein dispute`rdf:type`

Triple Nested Loop^[2]all time · B27efc86 7008 4384 852a 049d06d255cb
Code Pattern^[4]all time · Ab309b28 E3c5 4bb8 Bbea 8ad22dd49cf7
Programming Pattern^[5]sourceall time · C0f00081 8803 4769 B3dc 7642832fcf0a
Iteration Pattern^[6]all time · 0d367f34 7f5d 4a1b 8f23 3943751f9eb9
Loop Structure^[7]all time · E0132e2b 72f6 4f78 Accb Ecb30e4872df
Loop Structure^[8]sourceall time · C8102774 0736 45ab 8d51 87fae35d0377
Control Structure^[9]all time · 901bbb1a 244d 441d B46c Db2b12f37dda
Loop Structure^[10]all time · C9baa714 Fb6f 4a4e A32c 8544bdaa25ed
Nested Loop Structure^[11]all time · 2bbf96fc 0aaa 4f43 99f5 59729807ae97
Control Structure^[13]all time · 8a4993f4 F608 4dde Bd3d 4ddc74b8b9ff

Inbound mentions (17)

Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.

`avoids`Avoids(2)

Code Snippetex:code-snippet
Flatten the Listex:flatten-the-list

`contains`Contains(2)

Code Blockex:code-block
Test Scriptex:test-script

`hasStructure`Has Structure(2)

Training Loopex:training-loop
Training Loopex:training-loop

`usedIn`Used in(2)

Batch Sizesex:batch-sizes
Worker Countsex:worker-counts

`containsLoop`Contains Loop(1)

Test Documentation Accuracyex:test-documentation-accuracy

`containsLoopNesting`Contains Loop Nesting(1)

Python Code Blockex:python-code-block

`controlFlow`Control Flow(1)

Code Snippetex:code-snippet

`focusesOn`Focuses on(1)

Root Cause Analysisex:root-cause-analysis

`iterates`Iterates(1)

Levenshtein Distance Functionex:levenshtein-distance-function

`locatedIn`Located in(1)

Inefficiencyex:inefficiency

`processedBy`Processed by(1)

Term Frequency Calculationex:term-frequency-calculation

`replaces`Replaces(1)

Flatten the Listex:flatten-the-list

`resultsFrom`Results From(1)

Quadratic Complexityex:quadratic-complexity

Other facts (25)

The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.

25 facts

Predicate	Value	Ref
Outer Loop	Epoch Loop	[8]
Outer Loop	Directory Iteration	[9]
Outer Loop	Threshold Loop	[10]
Outer Loop	Threshold Loop	[11]
Outer Loop	I	[12]
Inner Loop	Batch Loop	[8]
Inner Loop	File Iteration	[9]
Inner Loop	Trial Loop	[10]
Inner Loop	Trials Loop	[11]
Iterable	Batch Sizes	[13]
Iterable	Worker Counts	[13]
Combines	Batch Sizes	[13]
Combines	Worker Counts	[13]
Outermost	Token Iteration	[2]
Middle	Synset Iteration	[2]
Innermost	Lemma Iteration	[2]
Contributes to	Inefficiency	[3]
Time Complexity	O-n-squared	[3]
Complexity Class	quadratic	[3]
Inefficiency Type	computational-overhead	[3]
Has Outer Loop	Document Iteration	[6]
Has Inner Loop	Term Iteration	[6]
Causes	Computational Inefficiency	[6]
Outer Loop Variable	batch_size	[13]
Inner Loop Variable	worker_count	[13]

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

36 undated

—

labelbeam/b3a0f03a-c138-41e0-9434-0946421a9c0e

nested for loops

—

typebeam/b27efc86-7008-4384-852a-049d06d255cb

ex:TripleNestedLoop

—

outermostbeam/b27efc86-7008-4384-852a-049d06d255cb

ex:token-iteration

—

middlebeam/b27efc86-7008-4384-852a-049d06d255cb

ex:synset-iteration

—

innermostbeam/b27efc86-7008-4384-852a-049d06d255cb

ex:lemma-iteration

—

contributesTobeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3

ex:inefficiency

—

timeComplexitybeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3

O-n-squared

—

typebeam/ab309b28-e3c5-4bb8-bbea-8ad22dd49cf7

ex:CodePattern

—

complexityClassbeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3

quadratic

—

inefficiencyTypebeam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3

computational-overhead

—

typebeam/c0f00081-8803-4769-b3dc-7642832fcf0a

ex:ProgrammingPattern

—

typebeam/0d367f34-7f5d-4a1b-8f23-3943751f9eb9

ex:Iteration-Pattern

—

hasOuterLoopbeam/0d367f34-7f5d-4a1b-8f23-3943751f9eb9

ex:document-iteration

—

hasInnerLoopbeam/0d367f34-7f5d-4a1b-8f23-3943751f9eb9

ex:term-iteration

—

causesbeam/0d367f34-7f5d-4a1b-8f23-3943751f9eb9

ex:computational-inefficiency

—

typebeam/e0132e2b-72f6-4f78-accb-ecb30e4872df

ex:LoopStructure

—

typebeam/c8102774-0736-45ab-8d51-87fae35d0377

ex:LoopStructure

—

outerLoopbeam/c8102774-0736-45ab-8d51-87fae35d0377

ex:epoch-loop

—

innerLoopbeam/c8102774-0736-45ab-8d51-87fae35d0377

ex:batch-loop

—

typebeam/901bbb1a-244d-441d-b46c-db2b12f37dda

ex:ControlStructure

—

outerLoopbeam/901bbb1a-244d-441d-b46c-db2b12f37dda

ex:directory-iteration

—

innerLoopbeam/901bbb1a-244d-441d-b46c-db2b12f37dda

ex:file-iteration

—

typebeam/c9baa714-fb6f-4a4e-a32c-8544bdaa25ed

ex:LoopStructure

—

outerLoopbeam/c9baa714-fb6f-4a4e-a32c-8544bdaa25ed

ex:threshold-loop

—

innerLoopbeam/c9baa714-fb6f-4a4e-a32c-8544bdaa25ed

ex:trial-loop

—

typebeam/2bbf96fc-0aaa-4f43-99f5-59729807ae97

ex:NestedLoopStructure

—

outerLoopbeam/2bbf96fc-0aaa-4f43-99f5-59729807ae97

ex:threshold-loop

—

innerLoopbeam/2bbf96fc-0aaa-4f43-99f5-59729807ae97

ex:trials-loop

—

outerLoopbeam/ffc8abcc-77b2-4a83-8215-f825e433c9b0

ex:i

—

typebeam/8a4993f4-f608-4dde-bd3d-4ddc74b8b9ff

ex:ControlStructure

—

outerLoopVariablebeam/8a4993f4-f608-4dde-bd3d-4ddc74b8b9ff

batch_size

—

innerLoopVariablebeam/8a4993f4-f608-4dde-bd3d-4ddc74b8b9ff

worker_count

—

iterablebeam/8a4993f4-f608-4dde-bd3d-4ddc74b8b9ff

ex:batch-sizes

—

iterablebeam/8a4993f4-f608-4dde-bd3d-4ddc74b8b9ff

ex:worker-counts

—

combinesbeam/8a4993f4-f608-4dde-bd3d-4ddc74b8b9ff

ex:batch-sizes

—

combinesbeam/8a4993f4-f608-4dde-bd3d-4ddc74b8b9ff

ex:worker-counts

References (13)

13 references

[1]B3a0f03a C138 41e0 9434 0946421a9c0e1 fact
ctx:claims/beam/b3a0f03a-c138-41e0-9434-0946421a9c0e
- full textbeam-chunk
  text/plain1 KBdoc:beam/b3a0f03a-c138-41e0-9434-0946421a9c0e
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  h6i7j8k9l0m1n2o3p4q5r6s7t8u9v0w1x2y3z4a5b6c7d8e9f0g1h2i3j4k5l6m7n8o9p0q1r2s3t4u5v6w7x8y9z0a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6q7r8s9t0u1v2w3x4y5z6a7b8c9d0e1f2g3h4i5j6k7l8m9n0o1p2q3r4s5t6u7v8w9x0y1z2a3b4c5d6e7f8g9h0i1j2k3l4m5n6o7p8q9r0s1t2u3v4w5…
[2]B27efc86 7008 4384 852a 049d06d255cb4 facts
ctx:claims/beam/b27efc86-7008-4384-852a-049d06d255cb
- full textbeam-chunk
  text/plain1 KBdoc:beam/b27efc86-7008-4384-852a-049d06d255cb
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  entities = [(ent.text, ent.label_) for ent in doc.ents] # Extract synonyms for each token synonyms = [] for token in tokens: pos = get_wordnet_pos(nltk.pos_tag([token])[0][1]) synsets = wordnet.synsets(t…
[3]2e6c4965 E243 4c73 Bf56 0e0c2bd6daa34 facts
ctx:claims/beam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
- full textbeam-chunk
  text/plain1 KBdoc:beam/2e6c4965-e243-4c73-bf56-0e0c2bd6daa3
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  [Turn 8666] User: I've been digging into the bottlenecks of my sparse training code, and I've found that term frequency miscalculations are delaying 14% of the 6,000 training cycles by 350ms, I'm using the following code to calculate the te…
[4]Ab309b28 E3c5 4bb8 Bbea 8ad22dd49cf71 fact
ctx:claims/beam/ab309b28-e3c5-4bb8-bbea-8ad22dd49cf7
- full textbeam-chunk
  text/plain1 KBdoc:beam/ab309b28-e3c5-4bb8-bbea-8ad22dd49cf7
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  1. **Nested Loops**: The nested loops iterate over each document and each term within the document, which can be inefficient for large datasets. 2. **Dictionary Operations**: Dictionary lookups and insertions can be costly, especially if th…
[5]C0f00081 8803 4769 B3dc 7642832fcf0a1 fact
ctx:claims/beam/c0f00081-8803-4769-b3dc-7642832fcf0a
- full textbeam-chunk
  text/plain1 KBdoc:beam/c0f00081-8803-4769-b3dc-7642832fcf0a
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  ["term1", "term2", "term3"], ["term2", "term3", "term4"], ["term1", "term2", "term3", "term4"] ] # Calculate the term frequencies term_frequencies = calculate_term_frequencies(documents) print(term_frequencies) ``` ### Explana…
[6]0d367f34 7f5d 4a1b 8f23 3943751f9eb94 facts
ctx:claims/beam/0d367f34-7f5d-4a1b-8f23-3943751f9eb9
- full textbeam-chunk
  text/plain1 KBdoc:beam/0d367f34-7f5d-4a1b-8f23-3943751f9eb9
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  Can you help me identify the root cause of this issue and provide a solution to improve the performance by using a more efficient data structure, such as a hash table? ->-> 4,21 [Turn 8679] Assistant: Certainly! The bottleneck in your term…
[7]E0132e2b 72f6 4f78 Accb Ecb30e4872df1 fact
ctx:claims/beam/e0132e2b-72f6-4f78-accb-ecb30e4872df
[8]C8102774 0736 45ab 8d51 87fae35d03773 facts
ctx:claims/beam/c8102774-0736-45ab-8d51-87fae35d0377
- full textbeam-chunk
  text/plain1 KBdoc:beam/c8102774-0736-45ab-8d51-87fae35d0377
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  for epoch in range(100): for batch in data_loader: inputs = batch['query'].float().to(device) labels = batch['label'].long().to(device) optimizer.zero_grad() outputs = model(input…
[9]901bbb1a 244d 441d B46c Db2b12f37dda3 facts
ctx:claims/beam/901bbb1a-244d-441d-b46c-db2b12f37dda
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  text/plain1 KBdoc:beam/901bbb1a-244d-441d-b46c-db2b12f37dda
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  completed_operations += sum(1 for op in operations if 'Completed' in content) self.assertGreaterEqual(completed_operations, int(self.completed_percentage * self.expected_operations), …
[10]C9baa714 Fb6f 4a4e A32c 8544bdaa25ed3 facts
ctx:claims/beam/c9baa714-fb6f-4a4e-a32c-8544bdaa25ed
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  test_terms = ["term1", "term2", "term3"] * 500 # Thresholds to test thresholds = [0.8, .85, .9, .95] # Number of trials to average over num_trials = 10 # Dictionary to store precision results precision_results = {} for threshold in thre…
[11]2bbf96fc 0aaa 4f43 99f5 59729807ae973 facts
ctx:claims/beam/2bbf96fc-0aaa-4f43-99f5-59729807ae97
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  [Turn 10085] Assistant: To test more thresholds, you can simply extend the list of thresholds you want to evaluate. You can add as many thresholds as you need to the `thresholds` list. Here's how you can modify the code to include additiona…
[12]Ffc8abcc 77b2 4a83 8215 F825e433c9b01 fact
ctx:claims/beam/ffc8abcc-77b2-4a83-8215-f825e433c9b0
[13]8a4993f4 F608 4dde Bd3d 4ddc74b8b9ff7 facts
ctx:claims/beam/8a4993f4-f608-4dde-bd3d-4ddc74b8b9ff
- full textbeam-chunk
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  # Test the implementation with different query loads test_queries = ["What is the meening of life?"] * 2500 # Example queries # Test with different batch sizes and worker counts batch_sizes = [100, 200, 500, 1000, 2500] worker_counts = [5…

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nested for loops

Rdf:typein disputerdf:type

Inbound mentions (17)

avoidsAvoids(2)

containsContains(2)

hasStructureHas Structure(2)

usedInUsed in(2)

containsLoopContains Loop(1)

containsLoopNestingContains Loop Nesting(1)

controlFlowControl Flow(1)

focusesOnFocuses on(1)

iteratesIterates(1)

locatedInLocated in(1)

processedByProcessed by(1)

replacesReplaces(1)

resultsFromResults From(1)

Other facts (25)

Timeline

References (13)

See also

Keep researching

Rdf:typein dispute`rdf:type`

`avoids`Avoids(2)

`contains`Contains(2)

`hasStructure`Has Structure(2)

`usedIn`Used in(2)

`containsLoop`Contains Loop(1)

`containsLoopNesting`Contains Loop Nesting(1)

`controlFlow`Control Flow(1)

`focusesOn`Focuses on(1)

`iterates`Iterates(1)

`locatedIn`Located in(1)

`processedBy`Processed by(1)

`replaces`Replaces(1)

`resultsFrom`Results From(1)