Files
ruvnet--RuView/vendor/sublinear-time-solver/dist/core/solver.d.ts
T
ruv 4b1005524e feat: complete vendor repos, add edge intelligence and WASM modules
- Add 154 missing vendor files (gitignore was filtering them)
  - vendor/midstream: 564 files (was 561)
  - vendor/sublinear-time-solver: 1190 files (was 1039)
- Add ESP32 edge processing (ADR-039): presence, vitals, fall detection
- Add WASM programmable sensing (ADR-040/041) with wasm3 runtime
- Add firmware CI workflow (.github/workflows/firmware-ci.yml)
- Add wifi-densepose-wasm-edge crate for edge WASM modules
- Update sensing server, provision.py, UI components

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-02 23:53:25 -05:00

67 lines
2.0 KiB
TypeScript

/**
* Core solver algorithms for asymmetric diagonally dominant systems
* Implements Neumann series, random walks, and push methods
*/
import { Matrix, Vector, SolverConfig, SolverResult, EstimationConfig, PageRankConfig, ProgressCallback } from './types.js';
export declare class SublinearSolver {
private config;
private performanceMonitor;
private convergenceChecker;
private timeoutController?;
private wasmAccelerated;
private wasmModules;
constructor(config: SolverConfig);
private initializeWasm;
private validateConfig;
/**
* Solve ADD system Mx = b using specified method
*/
solve(matrix: Matrix, vector: Vector, progressCallback?: ProgressCallback): Promise<SolverResult>;
/**
* Solve using Neumann series expansion
* x* = (I - D^(-1)R)^(-1) D^(-1) b = sum_{k=0}^∞ (D^(-1)R)^k D^(-1) b
*/
private solveNeumann;
/**
* Compute off-diagonal matrix-vector multiplication: (M - D) * v
* This computes R*v where R = M - D (off-diagonal part of matrix)
*/
private computeOffDiagonalMultiply;
/**
* Solve using random walk sampling
*/
private solveRandomWalk;
/**
* Create transition matrix for random walks
*/
private createTransitionMatrix;
/**
* Perform a single random walk
*/
private performRandomWalk;
/**
* Solve using forward push method
*/
private solveForwardPush;
/**
* Solve using backward push method
*/
private solveBackwardPush;
/**
* Solve using bidirectional approach (combine forward and backward)
*/
private solveBidirectional;
/**
* Estimate a single entry of the solution M^(-1)b
*/
estimateEntry(matrix: Matrix, vector: Vector, config: EstimationConfig): Promise<{
estimate: number;
variance: number;
confidence: number;
}>;
/**
* Compute PageRank using the solver
*/
computePageRank(adjacency: Matrix, config: PageRankConfig): Promise<Vector>;
}