ricable sublinear-time-solver
Sublinear-time algorithms toolkit providing PageRank approximation, matrix sketching, approximate nearest neighbors, streaming statistics, and chaos analysis for processing massive datasets with sub-linear time complexity.
Quick Reference
| Task | Code |
|---|---|
| Install | npx sublinear-time-solver@latest |
| Import | import { SublinearSolver } from 'sublinear-time-solver'; |
| Create | const solver = new SublinearSolver(); |
| PageRank | const rank = await solver.pageRank(graph); |
| Sketch | const sketch = await solver.matrixSketch(data); |
| ANN | const neighbors = await solver.approximateNN(query); |
Installation
Hub install (recommended): npx neural-trader@latest includes this package.
Standalone: npx sublinear-time-solver@latest
See Installation Guide for the full ecosystem.
Key API
SublinearSolver
The main solver class providing sublinear algorithm implementations.
import { SublinearSolver } from 'sublinear-time-solver';
const solver = new SublinearSolver({
precision: 'high',
seed: 42,
});
Constructor Options:
| Option | Type | Default | Description |
|---|---|---|---|
precision |
string |
'medium' |
Precision: 'low', 'medium', 'high' |
seed |
number |
random | Random seed for reproducibility |
maxIterations |
number |
1000 |
Maximum iterations for iterative algorithms |
epsilon |
number |
0.01 |
Approximation error bound |
delta |
number |
0.05 |
Failure probability bound |
Methods:
| Method | Returns | Description |
|---|---|---|
pageRank(graph, opts?) |
Promise<PageRankResult> |
Approximate PageRank |
matrixSketch(data, opts?) |
Promise<MatrixSketch> |
Matrix sketching |
approximateNN(query, opts?) |
Promise<NNResult[]> |
Approximate nearest neighbors |
streamingMean(stream) |
Promise<number> |
Streaming mean computation |
streamingQuantile(stream, q) |
Promise<number> |
Streaming quantile estimation |
countMin(stream, opts?) |
Promise<CountMinSketch> |
Count-Min Sketch |
hyperLogLog(stream) |
Promise<number> |
Cardinality estimation |
sparseFFT(signal, k) |
Promise<SparseFFTResult> |
Sparse FFT (k-sparse) |
PageRank
Approximate PageRank using random walks.
import { PageRank } from 'sublinear-time-solver';
const pr = new PageRank({
damping: 0.85,
epsilon: 0.001,
maxIterations: 100,
});
const result = await pr.compute(graph);
Constructor Options:
| Option | Type | Default | Description |
|---|---|---|---|
damping |
number |
0.85 |
Damping factor |
epsilon |
number |
0.001 |
Convergence threshold |
maxIterations |
number |
100 |
Maximum iterations |
algorithm |
string |
'power' |
Method: 'power', 'montecarlo', 'push' |
personalizedNode |
number |
undefined |
Personalized PageRank source node |
MatrixSketch
Approximate low-rank matrix decomposition.
import { MatrixSketch } from 'sublinear-time-solver';
const sketch = new MatrixSketch({ sketchSize: 100, method: 'frequent-directions' });
const result = await sketch.fit(dataMatrix);
const approx = await sketch.reconstruct();
Constructor Options:
| Option | Type | Default | Description |
|---|---|---|---|
sketchSize |
number |
100 |
Sketch dimension |
method |
string |
'frequent-directions' |
Method: 'frequent-directions', 'random-projection', 'count-sketch' |
StreamingStats
Online streaming statistical computations.
import { StreamingStats } from 'sublinear-time-solver';
const stats = new StreamingStats();
for (const value of dataStream) {
stats.update(value);
}
console.log(`Mean: ${stats.mean}, Variance: ${stats.variance}`);
Methods:
| Method | Returns | Description |
|---|---|---|
update(value) |
void |
Add a value to the stream |
mean |
number |
Current mean |
variance |
number |
Current variance (Welford) |
quantile(q) |
number |
Approximate quantile |
count |
number |
Total elements seen |
min / max |
number |
Min/max values |
Common Patterns
Large-Scale Graph PageRank
import { SublinearSolver } from 'sublinear-time-solver';
const solver = new SublinearSolver({ precision: 'high' });
const rank = await solver.pageRank(webGraph, {
topK: 100,
algorithm: 'montecarlo',
walks: 10000,
});
rank.topNodes.forEach(node => {
console.log(`${node.id}: ${node.score.toFixed(6)}`);
});
Streaming Cardinality Estimation
import { SublinearSolver } from 'sublinear-time-solver';
const solver = new SublinearSolver();
const cardinality = await solver.hyperLogLog(userIdStream);
console.log(`Approximate unique users: ${cardinality}`);
Dimensionality Reduction
import { MatrixSketch } from 'sublinear-time-solver';
const sketch = new MatrixSketch({ sketchSize: 50, method: 'random-projection' });
const reduced = await sketch.fit(highDimData);
// reduced: [n, 50] instead of [n, 10000]
RAN DDD Context
Bounded Context: RANO Optimization
References
- API reference: See references/commands.md
- Full README
- npm