SolvLRDE: Examples
Run journeys 01→12 in order. Each page below is a full walkthrough: what the scenario teaches, which APIs matter, the important code pattern, act-by-act narration, and commands to run locally.
Examples source: Core journeys (
01–06) and vertical SKU scripts (skus/sku_*.py) ship in the SolvLRDE release bundle. The 30-day trial wheel is generic; SKU walkthroughs show how each vertical calls the samesolvlrdeAPI. Choosing a SKU? See Vertical SKUs for who each license is for, the problem it solves, and how trials differ from purchased seats. Beta release. Products in this catalog other than the SolvSRK family and SolvScout / SolvTune are beta — suitable for trials and evaluation; APIs and packaging may change before GA. Do not deploy beta builds in production programs without a signed agreement with Resonix. Activate a trial license before running examples — see Install and Licensing.
What the journeys teach
Many engineering problems are linear time-dependent systems — heat diffusion, power-grid transients, LTI control, pricing kernels — but teams time-step them because that is what their toolchain knows. Time-stepping cost grows with horizon T and collapses on stiffness.
SolvLRDE evaluates the solution at time T directly via Talbot contour Laplace inversion. Cost is essentially independent of T and the method is stiffness-immune for problems in scope. Use SolvLRDE for linear systems; use SolvSRK for nonlinear ODEs — mixing them is the most common integration mistake.
Journey index
| # | Journey | What you learn |
|---|---|---|
| 01 | First contact | Verify install and license, read machine code, and solve the smallest direct linear problem (y'=-y at T=1) with a known … |
| 02 | Core path | The workhorse solvlrde.solve(A, y0, T) call, reading n_solves in the receipt, and constant forcing with b=. |
| 03 | Configuration (contour + N) | Tune n_talbot and nu, select CONTOUR_* shapes, and pass cfg= into solve. |
| 04 | Time-independent cost | Same stiff operator at T=1, 1e3, and 1e6 — n_solves and wall_s stay flat because T does not appear in the cost model. |
| 05 | Banded + DAE | solve_banded on tridiagonal MOL operators and reduce_dae for linear descriptor systems. |
| 06 | Limits | Constant-A requirement, when to use SolvSRK instead, and survived=False on blow-up. |
| 07 | SolvLRDE-Grid | EMTP-style segment solves after switching events — constant A per topology, stitch at breaker times. |
| 08 | SolvLRDE-Pricing | Hull-White OU propagation across maturities — inner linear solve in xVA/ALM stacks. |
| 09 | SolvLRDE-Control | LTI step response at arbitrary T from state-space A — self-serve 30-day trial adoption path. |
| 10 | SolvLRDE-Climate | Tangent-linear fast/slow modes at century-scale T inside CMIP-class workflows. |
| 11 | SolvLRDE-Thermal | 1-D heat equation MOL via solve_banded on a tridiagonal spatial operator. |
| 12 | SolvLRDE-Specialty | CTMC reliability and bespoke linear propagators with generator matrix Q. |
Start with Journey 01 if this is your first install.
Set SOLVLRDE_QUIET=1 only when you want silent CLI runs (no narration).
Example bundle
Every journey is a domain scenario with narrated acts — run the guided tour first to see what each number means, then copy the production one-liner into your pipeline.
| Resource | Purpose |
|---|---|
PROGRESSION.md | Core runbook — journeys 01→06 with dual commands |
SKU_PROGRESSION.md | Vertical SKU runbook — Grid, Pricing, Control, Climate, Thermal, Specialty |
COVERAGE.md | Capability matrix — which APIs each journey exercises |
APPLICATIONS.md | Where the product applies in real programs |
run_examples.py | Interactive menu for core journeys |
run_sku_examples.py | Interactive menu for vertical SKU walkthroughs |
Guided journeys
| # | Script | Scenario |
|---|---|---|
| 01 | 01_first_contact.py | Version, license, and the smallest direct linear solve |
| 02 | 02_core_path.py | Solve y' = A y directly at a target time T |
| 03 | 03_configuration.py | Tune the Talbot contour: node count, nu, and contour shape |
| 04 | 04_time_independence.py | The headline property: cost does not grow with T |
| 05 | 05_banded_dae.py | Exploit sparsity, and reduce a linear DAE to canonical form |
| 06 | 06_limits.py | What SolvLRDE is for, and what belongs to SolvSRK / SolvJump |
| 07 | sku_grid.py | After a switching event, each EMTP segment is a constant linear system y' = A y on bus vol… |
| 08 | sku_pricing.py | xVA, FRTB IMA, and ALM stacks spend most of their wall clock on repeated linear PDE / OU p… |
| 09 | sku_control.py | Transfer functions, step responses, and Gramian queries reduce to evaluating the resolvent… |
| 10 | sku_climate.py | Fully nonlinear primitive-equation dynamics stay in the host model. Radiative transfer, tr… |
| 11 | sku_thermal.py | Discretize u_t = u_xx on a rod with second differences. The semidiscrete operator is tridi… |
| 12 | sku_specialty.py | Queues, reliability CTMCs, and bespoke linear propagators share the same API: constant gen… |
Run from the python/ directory after install and license activation.
Set SOLVLRDE_QUIET=1 only when you want silent CLI runs (no narration).