solver-numerics

Solve nonlinear MNA circuit models using Newton-Raphson iteration with implicit time stepping.

Updated Apr 7, 2026
One-click install
npx skills add https://github.com/lgili/skillex --skill solver-numerics
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: solver-numerics
Source: https://github.com/lgili/skillex/tree/main/skills/solver-numerics
Command: npx skills add https://github.com/lgili/skillex --skill solver-numerics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Numerical cores for discrete-time circuit simulations require robust nonlinear solving and stable time integration. This skill provides guidance on using implicit methods (trapezoidal, backward Euler, GEAR/BDF), Newton-Raphson convergence handling, and sparse linear algebra using Eigen or UMFPACK to debug slow or oscillatory behavior in stiff circuits.

Core Features & Use Cases

  • Implicit integration options (trapezoidal, backward Euler, GEAR/BDF) for stiff, switching, and nonlinear networks.
  • Newton-Raphson iteration with convergence checks and damping strategies to ensure reliable stepping.
  • Sparse linear solver guidance (Eigen, UMFPACK) and topology-change factorization reuse to optimize performance.
  • Diagnostic routines for convergence, step-size selection, and accuracy validation with history states.

Quick Start

Run a time-domain simulation with implicit integration to observe nonlinear MNA circuit behavior under switching events.

Frequently Asked Questions about solver-numerics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I solve nonlinear circuit equations using Newton-Raphson and MNA?

Solve nonlinear circuit equations by applying Newton-Raphson iterative linearization to Modified Nodal Analysis (MNA) formulations during implicit time stepping, using convergence checks and damping strategies to ensure reliable iteration steps.

What is the best way to handle stiff circuit simulations with implicit integration?

Handle stiff circuit simulations by applying implicit integration methods like trapezoidal, backward Euler, or GEAR/BDF, which provide the numerical stability required for stiff, switching, and nonlinear networks.

How do I optimize sparse linear solver performance for circuit simulations?

Optimize sparse linear solver performance by using Eigen or UMFPACK backends and reusing matrix factorizations across switching events to handle topology changes efficiently without full refactorization.

Why does my Newton-Raphson iteration fail to converge during discrete-time circuit simulation?

Newton-Raphson iteration fails to converge due to oscillatory behavior in stiff circuits; applying damping strategies, convergence checks, and implicit integration like backward Euler ensures reliable stepping.

Can I use Eigen or UMFPACK for sparse matrix solving in SPICE-like circuit solvers?

Yes, you can use Eigen or UMFPACK as reusable sparse solver backends for SPICE-like Newton-Raphson convergence handling, optimizing sparse linear algebra across nonlinear components.

Do I need diagnostic routines for step-size selection in nonlinear circuit simulation?

Yes, you need diagnostic routines for step-size selection and accuracy validation to monitor convergence and maintain history states during nonlinear MNA circuit behavior under switching events.