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high density zero-obstacle solver","maintainers":[{"name":"seveibar","email":"seveibar@gmail.com"}],"readme":"# @tscircuit/high-density-a01\n\nA high density zero-obstacle solver\n\nThis is a @tscircuit/solver-utils BaseSolver-compatible solver with the following\nproperties:\n\n- Multi-layer\n- Grid-based\n- Supports High Density Types from tscircuit-autorouter\n- Rip'n'Replace with History (@tscircuit/hypergraph-inspired)\n- Via Penalty and Trace Penalty Map\n\n<p align=\"center\">\n  <img src=\"https://github.com/user-attachments/assets/4463e513-b231-456d-953d-a15c3d9ae376\" width=\"320\" />\n</p>\n\n## Usage\n\nThe package exports the solver classes directly:\n\n```ts\nimport {\n  HighDensitySolverA03,\n  HighDensitySolverA05,\n} from \"@tscircuit/high-density-a01\"\n```\n\n### A03\n\nUse `HighDensitySolverA03` for the baseline high-density solver:\n\n```ts\nconst solver = new HighDensitySolverA03({\n  nodeWithPortPoints,\n  highResolutionCellSize: 0.1,\n  highResolutionCellThickness: 8,\n  lowResolutionCellSize: 0.4,\n  traceThickness: 0.1,\n  traceMargin: 0.15,\n  viaDiameter: 0.3,\n  viaMinDistFromBorder: 0.15,\n  maxCellCount: 200_000,\n  stepMultiplier: 4,\n\n  hyperParameters: {\n    shuffleSeed: 0,\n    ripCost: 8,\n    ripTracePenalty: 0.5,\n    ripViaPenalty: 0.75,\n    viaBaseCost: 0.1,\n    greedyMultiplier: 1.5,\n  },\n\n  // Optional initial penalty map\n  // initialPenaltyFn: ({ x, y, px, py, row, col, region }) => ...\n})\n\nsolver.solve()\nconst routes = solver.getOutput()\n```\n\n### A05\n\nUse `HighDensitySolverA05` when you want A03-style routing plus route\nnormalization and force-directed reflow after each solved route:\n\n```ts\nconst solver = new HighDensitySolverA05({\n  nodeWithPortPoints,\n  highResolutionCellSize: 0.1,\n  highResolutionCellThickness: 8,\n  lowResolutionCellSize: 0.4,\n  traceThickness: 0.1,\n  traceMargin: 0.15,\n  viaDiameter: 0.3,\n  viaMinDistFromBorder: 0.15,\n\n  // A05 defaults\n  postRouteSegmentCount: 16,\n  postRouteForceDirectedSteps: 20,\n\n  // Initial border-avoidance bias\n  borderPenaltyStrength: 0.25,\n  borderPenaltyFalloff: 0.12,\n})\n\nsolver.solve()\nconst routes = solver.getOutput()\n```\n\nNotes:\n\n- `HighDensitySolverA05` uses the same routing hyperparameters as A03 by default.\n- `postRouteSegmentCount` counts vias toward the total segment budget.\n- The default A05 initial penalty map discourages routing too close to the node\n  border. Set `borderPenaltyStrength: 0` to disable that bias.\n- Providing `initialPenaltyFn` overrides the built-in A05 border penalty.\n- The output routes preserve the exact user-provided endpoints.\n\n## How it works\n\nFor A03/A05, we form a two-resolution grid using\n`highResolutionCellSize`, `highResolutionCellThickness`, and\n`lowResolutionCellSize`.\n\nWe compute the initial penalty map from `initialPenaltyFn`. This function sets\nan additional cost of traversal for a cell. It receives `x`/`y` in board\ncoordinates, and `px`/`py` in `[0,1]` relative to the node bounds.\n\nWe shuffle the trace order based on the shuffle seed.\n\nWe run an A\\* search for each path from the `start` to the `end`. During\nexploration, we consider both used and unused cells. Used cells incur rip costs\nand trace/via penalties, while vias allow moving between any available layers.\nA path that rips the same trace only pays `ripCost` once, so the search tracks\nwhich traces have already been ripped along that candidate path.\n\nWhen we reach the `end` of a path, we mark that route as solved and apply its\noccupied cells to the congestion structure. Vias occupy more cells based on\n`viaDiameter`. If a solved route displaced other routes, those routes are ripped\nout and added back to the unsolved queue.\n\nFor A05, after each solved route we:\n\n1. Normalize all solved routes to a fixed total segment count.\n2. Run a force-directed reflow pass over the solved route set.\n3. Rebuild occupancy from the updated geometry before routing the next trace.\n\nThis creates additional room for later routes at the cost of extra per-route\nwork.\n\n## Benchmarks\n\nUseful benchmark commands:\n\n```sh\nbun run scripts/run-dataset02-benchmark-a03.ts --concurrency=4\nbun run scripts/run-dataset02-benchmark-a05.ts --concurrency=4\n```\n\nA05 tuning examples:\n\n```sh\nbun run scripts/run-dataset02-benchmark-a05.ts --concurrency=4 --border-penalty-strength=0.25 --border-penalty-falloff=0.12\nbun run scripts/run-dataset02-benchmark-a05.ts --concurrency=4 --rip-cost=8 --greedy-multiplier=1.5\n```\n\n## high-density-a02\n\nThe high-density-a02 solver is a variant that uses an inner and outer grid to reduce the number of cells\nwhile still allowing high density edges\n\n![a02](https://private-user-images.githubusercontent.com/1910070/563350353-45e8fbc3-a666-4927-a200-8bac665c2ee1.png?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.EcpBaVzRhqRLgmRwX50ekIZ6_O9PD427VvrwnYV4sVY)\n","readmeFilename":"README.md"}