sublinear-time-solver

This MCP server exposes the Sublinear Time Solver tool as a unified MCP interface. It bundles a collection of sublinear-time linear algebra routines and related analytical utilities (matrix generation, solves, analysis, and comparisons) that are accelerated with WebAssembly where possible and designed for composition across tools. When you run it through the MCP entry point, you can invoke subcommands such as generate, solve, analyze, and help-examples to construct test matrices, assemble vectors, solve linear systems, and compare solver strategies. The toolset emphasizes true sublinear algorithms (e.g., Neumann series, forward/backward push, Johnson-Lindenstrauss based reductions) and can also perform matrix property analysis to pick the most suitable solver pathway automatically. Use the MCP wrapper to chain multiple operations in a single prompt, enabling multi-step experiments without leaving your orchestration layer.

Typical workflows include: generating a diagonally dominant test matrix, creating a matching right-hand side vector, solving the linear system with various solver methods (neumann, forward-push, backward-push, random-walk), and then analyzing the results for stability and error bounds. The CLI exposes commands like generate, solve, analyze, and help-examples; you can also request full analysis of matrix properties to guide algorithm choice. This makes it suitable for research experiments, algorithm benchmarking, and plug-in orchestration within larger AI/ML pipelines that require robust linear-algebra subroutines in a sublinear regime.

Prerequisites:

• Node.js >= 14 (and npm)
• Internet access to fetch the MCP package via npx

Installation steps:

1. Install Node.js and npm from https://nodejs.org/ if you don’t already have them.
2. Run the MCP server via npx (no global install required):

# Start the MCP server interface for sublinear-time-solver
npx sublinear-time-solver mcp

3. Optional: pin a specific version in your automation by using the package name with a version, e.g.:

npx sublinear-time-solver@1.4.1 mcp

4. In your environment, you can also invoke the CLI locally by calling the underlying package once installed:

npm install -g sublinear-time-solver
sublinear-time-solver mcp

Prerequisites recap: Node.js and npm installed, internet access to fetch the package, and a runtime environment capable of executing npx commands.

Tips and considerations:

• The MCP entry point supports chaining commands; use it to build end-to-end experiments (e.g., generate matrix -> solve with neumann -> analyze properties -> adjust method selection).
• If you encounter memory or timeout issues, consider using the matrix analysis options first to guide method selection and reduce unnecessary computations.
• The tool emphasizes sublinear algorithms with WASM acceleration; for very large sparse matrices, prefer Neumann series or forward/backward push variants when appropriate.
• Environment variables are typically not required, but you can override defaults for paths or logging in your orchestration layer if needed. Check the tool’s help-examples for concrete usage patterns and default parameter values.
• Ensure your runtime has sufficient CPU and memory to handle matrix generation and solving tasks, especially when generating large test matrices (e.g., 1000x1000 or larger).