Sublinear Time Solver
Rust + WASM sublinear-time solver for asymmetric diagonally dominant systems. Exposes Neumann series, push, and hybrid random-walk algorithms with npm/npx CLI and Flow-Nexus HTTP streaming for swarm cost propagation and verification.
What is Sublinear Time Solver?
Sublinear Time Solver is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to rust + wasm sublinear-time solver for asymmetric diagonally dominant systems. exposes neumann series, push, and hybrid random-walk algorithms with npm/npx cli and flow-nexus http streaming for swarm c...
Rust + WASM sublinear-time solver for asymmetric diagonally dominant systems. Exposes Neumann series, push, and hybrid random-walk algorithms with npm/npx CLI and Flow-Nexus HTTP streaming for swarm cost propagation and verification.
This server falls under the Data Science & ML category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Rust + WASM sublinear-time solver for asymmetric diagonally
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx sublinear-time-solverConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Sublinear Time Solver
Sublinear Time Solver is a Rust and WebAssembly-powered MCP server that exposes over 40 mathematical tools for solving asymmetric diagonally dominant linear systems faster than standard O(n) methods. It implements Neumann series, Forward/Backward Push, and random-walk algorithms, making it suitable for large-scale graph Laplacian computations, PageRank, and network flow optimization problems. AI agents connected via MCP can generate test matrices, solve systems, analyze sparsity and condition numbers, and stream results through the Flow-Nexus HTTP interface.
Prerequisites
- Node.js 18 or later (for npx/npm execution and WASM runtime)
- npm or npx available in your PATH
- An MCP-compatible client such as Claude Desktop
- Sparse or diagonally dominant matrices for meaningful solver performance gains
Run immediately with npx (no install needed)
The fastest way to try the solver is via npx — no global install required. This downloads and starts the MCP server on demand.
npx sublinear-time-solver mcpInstall globally (optional, for frequent use)
Install the package globally to avoid download delays on each run and to use the CLI solver commands independently.
npm install -g sublinear-time-solverConfigure Claude Desktop
Add the MCP server to Claude Desktop's configuration file. The 'mcp' argument starts the server in MCP mode instead of CLI mode.
Generate a test matrix
Use the CLI to generate a diagonally dominant test matrix and a right-hand-side vector for testing the solver.
npx sublinear-time-solver generate -t diagonally-dominant -s 1000 -o matrix.jsonSolve a linear system
Run the solver on a generated matrix and vector, specifying the algorithm and output file.
npx sublinear-time-solver solve -m matrix.json -b vector.json --method neumann -o solution.jsonAnalyze a matrix
Run full analysis on a matrix to inspect sparsity, diagonal dominance, and condition number before solving.
npx sublinear-time-solver analyze -m matrix.json --fullSublinear Time Solver Examples
Client configuration
Claude Desktop configuration to run the Sublinear Time Solver as an MCP server using npx.
{
"mcpServers": {
"sublinear-solver": {
"command": "npx",
"args": ["sublinear-time-solver", "mcp"]
}
}
}Prompts to try
Ask the AI to use the solver tools for mathematical and graph analysis tasks.
- "Generate a 500x500 diagonally dominant matrix and solve it using the Neumann series method."
- "Compute the PageRank of my graph with a damping factor of 0.85."
- "Analyze this adjacency matrix for sparsity and estimate the condition number."
- "Solve this network flow optimization problem using the random-walk algorithm."
- "Compare the Neumann and push-based algorithms on a 1000-node graph and report convergence."Troubleshooting Sublinear Time Solver
npx sublinear-time-solver mcp hangs without starting
Ensure Node.js 18 or later is installed (node --version). The WASM binary requires a modern V8 engine. Try 'npx sublinear-time-solver --help' first to confirm the package downloads correctly.
Solver returns incorrect results or fails to converge
The Neumann series and push algorithms are optimized for diagonally dominant matrices. If your matrix is not diagonally dominant, convergence is not guaranteed. Use 'npx sublinear-time-solver analyze -m matrix.json --full' to verify diagonal dominance before solving.
MCP tools are not appearing in the client
Confirm you are passing the 'mcp' argument: 'npx sublinear-time-solver mcp'. Without it, the package runs in CLI mode and does not expose MCP tools.
Frequently Asked Questions about Sublinear Time Solver
What is Sublinear Time Solver?
Sublinear Time Solver is a Model Context Protocol (MCP) server that rust + wasm sublinear-time solver for asymmetric diagonally dominant systems. exposes neumann series, push, and hybrid random-walk algorithms with npm/npx cli and flow-nexus http streaming for swarm cost propagation and verification. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Sublinear Time Solver?
Follow the installation instructions on the Sublinear Time Solver GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Sublinear Time Solver?
Sublinear Time Solver works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Sublinear Time Solver free to use?
Yes, Sublinear Time Solver is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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Set Up Sublinear Time Solver in Your Editor
Choose your AI client for step-by-step setup instructions.
Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
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