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LP, MIP, and MINLP support","maintainers":[{"name":"arebos","email":"lent1510@gmail.com"}],"readme":"# SCIP.js\n\n**SCIP Optimization Solver compiled to WebAssembly**\n\nSolve Linear Programming (LP), Mixed Integer Programming (MIP), and **Mixed Integer Nonlinear Programming (MINLP)** problems directly in the browser or Node.js.\n\n## Features\n\n- **LP**: Linear Programming\n- **MIP**: Mixed Integer Programming (binary, integer variables)\n- **MINLP**: Mixed Integer Nonlinear Programming (quadratic, polynomial, nonlinear constraints)\n- **ZIMPL**: Full ZIMPL modeling language support for complex problems\n- **Zero Dependencies**: Pure WebAssembly, no native bindings\n- **Browser & Node.js**: Works everywhere JavaScript runs\n- **Web Worker Support**: Non-blocking solver for long computations\n- **TypeScript**: Full type definitions included\n\n## Installation\n\n```bash\nnpm install scip.js\n```\n\nOr use via CDN:\n\n```html\n<script type=\"module\">\n  import SCIP from 'https://unpkg.com/scip.js/dist/index.mjs';\n</script>\n```\n\n## Quick Start\n\n### Linear Programming (LP Format)\n\n```javascript\nimport SCIP from 'scip.js';\n\nconst result = await SCIP.solve(`\n  Minimize\n    obj: 2 x + 3 y\n  Subject To\n    c1: x + y >= 4\n    c2: 2 x + y >= 5\n  Bounds\n    x >= 0\n    y >= 0\n  End\n`);\n\nconsole.log(result.status);     // 'optimal'\nconsole.log(result.objective);  // 7.0\nconsole.log(result.variables);  // { x: 1, y: 3 }\n```\n\n### Nonlinear Programming (ZIMPL Format)\n\nFor **MINLP** and nonlinear problems, use ZIMPL format:\n\n```javascript\nimport SCIP from 'scip.js';\n\n// Quadratic optimization: minimize x² + y²\nconst result = await SCIP.solve(`\n  var x >= 0 <= 10;\n  var y >= 0 <= 10;\n  \n  minimize cost: x * x + y * y;\n  \n  subto c1: x + y >= 2;\n`, { format: 'zpl' });\n\nconsole.log(result.status);     // 'optimal'\nconsole.log(result.objective);  // 2.0\nconsole.log(result.variables);  // { x: 1, y: 1 }\n```\n\n## MINLP Examples\n\n### Quadratic Programming\n\n```javascript\n// Portfolio optimization with risk (quadratic)\nconst result = await SCIP.solve(`\n  set ASSETS := { \"A\", \"B\", \"C\" };\n  \n  param return[ASSETS] := <\"A\"> 0.12, <\"B\"> 0.08, <\"C\"> 0.15;\n  param risk[ASSETS] := <\"A\"> 0.20, <\"B\"> 0.10, <\"C\"> 0.30;\n  \n  var x[ASSETS] >= 0 <= 1;\n  \n  maximize portfolio_return: \n    sum <a> in ASSETS: return[a] * x[a] \n    - 0.5 * sum <a> in ASSETS: risk[a] * x[a] * x[a];\n  \n  subto budget: sum <a> in ASSETS: x[a] == 1;\n`, { format: 'zpl' });\n```\n\n### Mixed Integer Nonlinear (MINLP)\n\n```javascript\n// Facility location with economies of scale\nconst result = await SCIP.solve(`\n  set FACILITIES := { 1 to 5 };\n  set CUSTOMERS := { 1 to 10 };\n  \n  param demand[CUSTOMERS] := <1> 10, <2> 15, <3> 20, <4> 12, <5> 18,\n                             <6> 8, <7> 25, <8> 14, <9> 16, <10> 11;\n  \n  var open[FACILITIES] binary;           # 1 if facility is open\n  var flow[FACILITIES * CUSTOMERS] >= 0; # amount shipped\n  var capacity[FACILITIES] >= 0 <= 100;  # facility capacity\n  \n  minimize total_cost:\n    sum <f> in FACILITIES: 1000 * open[f]                    # fixed cost\n    + sum <f> in FACILITIES: 50 * sqrt(capacity[f])          # capacity cost (nonlinear)\n    + sum <f,c> in FACILITIES * CUSTOMERS: 2 * flow[f,c];    # transport cost\n  \n  subto satisfy_demand: \n    forall <c> in CUSTOMERS:\n      sum <f> in FACILITIES: flow[f,c] >= demand[c];\n  \n  subto capacity_limit:\n    forall <f> in FACILITIES:\n      sum <c> in CUSTOMERS: flow[f,c] <= capacity[f] * open[f];\n`, { format: 'zpl' });\n```\n\n### Polynomial Constraints\n\n```javascript\n// Nonlinear constraints with polynomials\nconst result = await SCIP.solve(`\n  var x >= -10 <= 10;\n  var y >= -10 <= 10;\n  \n  minimize obj: x + y;\n  \n  # Polynomial constraint: x³ + y³ >= 8\n  subto poly: x * x * x + y * y * y >= 8;\n  \n  # Circle constraint: x² + y² <= 25\n  subto circle: x * x + y * y <= 25;\n`, { format: 'zpl' });\n```\n\n## API Reference\n\n### `SCIP.solve(problem, options?)`\n\nSolve an optimization problem.\n\n**Parameters:**\n- `problem` (string): Problem definition\n- `options` (object, optional):\n  - `format`: `'lp'` | `'mps'` | `'zpl'` | `'cip'` (default: `'lp'`)\n    - `'lp'`: LP format (linear problems only)\n    - `'mps'`: MPS format (linear problems only)\n    - `'zpl'`: **ZIMPL format (supports MINLP, nonlinear)**\n    - `'cip'`: CIP format (SCIP's native format)\n  - `timeLimit`: Time limit in seconds (default: 3600)\n  - `gap`: Relative MIP gap tolerance (e.g., 0.01 for 1%)\n  - `params`: Additional SCIP parameters (e.g., `{ \"display/verblevel\": 4 }`)\n  - `parameters`: Legacy alias for `params`\n  - `initialSolution`: Warm start values `{ varName: value }`\n  - `cutoff`: Cutoff bound for pruning\n  - `onIncumbent`: Callback for new incumbent objective value\n  - `onNode`: Callback for node progress `{ dualBound, primalBound, nodes }`\n  - `verbose`: Legacy helper (maps to `display/verblevel` when not set in params)\n\n**Returns:** `Promise<Solution>`\n\n```typescript\ninterface Solution {\n  status: 'optimal' | 'infeasible' | 'unbounded' | 'timelimit' | 'unknown' | 'error';\n  objective: number | null;\n  variables: Record<string, number>;\n  statistics: {\n    solvingTime: number | null;\n    nodes: number | null;\n    iterations: number | null;\n    gap: number | null;\n  };\n}\n```\n\n### `SCIP.minimize(problem, options?)`\n\nConvenience wrapper that ensures the problem is minimized.\n\n### `SCIP.maximize(problem, options?)`\n\nConvenience wrapper that ensures the problem is maximized.\n\n### `SCIP.init(options?)`\n\nInitialize the SCIP WASM module. Called automatically on first solve.\n\n### `SCIP.version()`\n\nGet SCIP version information.\n\n## Format Reference\n\n### LP Format (Linear Only)\n\n```\n\\ Comments start with backslash\nMinimize (or Maximize)\n  obj: 2 x + 3 y - z\n\nSubject To\n  constraint1: x + y >= 10\n  constraint2: x - y <= 5\n\nBounds\n  0 <= x <= 100\n  y >= 0\n\nGeneral (Integer variables)\n  x\n\nBinary (0-1 variables)\n  y\n\nEnd\n```\n\n### ZIMPL Format (Supports Nonlinear)\n\n```zimpl\n# Sets\nset ITEMS := { \"apple\", \"banana\", \"orange\" };\n\n# Parameters\nparam weight[ITEMS] := <\"apple\"> 2, <\"banana\"> 3, <\"orange\"> 4;\nparam value[ITEMS] := <\"apple\"> 10, <\"banana\"> 15, <\"orange\"> 20;\n\n# Variables\nvar x[ITEMS] binary;          # binary variable\nvar y >= 0 <= 100;            # continuous variable\nvar z integer >= 0 <= 10;     # integer variable\n\n# Objective (can include nonlinear terms)\nmaximize profit: \n  sum <i> in ITEMS: value[i] * x[i] \n  - 0.1 * y * y;              # quadratic term\n\n# Constraints\nsubto capacity: \n  sum <i> in ITEMS: weight[i] * x[i] <= 10;\n\nsubto nonlinear_constraint:\n  y * y + z * z <= 50;        # quadratic constraint\n```\n\n## More Examples\n\n### Linear Programming\n\n```javascript\nconst result = await SCIP.minimize(`\n  obj: 100 x1 + 150 x2\n  Subject To\n    labor: 2 x1 + 3 x2 <= 120\n    materials: 4 x1 + 2 x2 <= 100\n  Bounds\n    x1 >= 0\n    x2 >= 0\n  End\n`);\n```\n\n### Mixed Integer Programming (Knapsack)\n\n```javascript\nconst result = await SCIP.maximize(`\n  obj: 60 item1 + 100 item2 + 120 item3\n  Subject To\n    weight: 10 item1 + 20 item2 + 30 item3 <= 50\n  Binary\n    item1 item2 item3\n  End\n`);\n\n// result.variables = { item2: 1, item3: 1 }\n// result.objective = 220\n```\n\n### With Time Limit, Gap, and Params\n\n```javascript\nconst result = await SCIP.solve(problem, {\n  format: 'zpl',\n  timeLimit: 60,  // 60 seconds\n  gap: 0.01,      // Stop when within 1% of optimal\n  params: {\n    'display/verblevel': 4,\n    'limits/solutions': 1\n  }\n});\n```\n\n### Warm Start and Cutoff\n\n```javascript\nconst result = await SCIP.solve(problem, {\n  format: 'zpl',\n  initialSolution: { x: 1, y: 0 },\n  cutoff: 100\n});\n```\n\n## Web Worker Usage\n\nFor long-running optimizations, use the Web Worker API to avoid blocking the main thread:\n\n```javascript\nimport { createWorkerSolver } from 'scip.js';\n\nconst solver = await createWorkerSolver();\n\n// Solve MINLP in background\nconst result = await solver.solve(nonlinearProblem, { \n  format: 'zpl',\n  timeLimit: 300 \n});\n\n// Clean up when done\nsolver.terminate();\n```\n\n## Building from Source\n\n### Prerequisites\n\n- Docker\n- Bash\n\n### Build\n\n```bash\n# Clone repository\ngit clone https://github.com/areb0s/scip.js\ncd scip.js\n\n# Build WASM (uses Docker, includes ZIMPL + GMP)\n./build.sh\n\n# Output in dist/\nls dist/\n# scip.js  scip.wasm  index.mjs  types.d.ts\n```\n\nThe build process:\n1. Downloads and compiles GMP (for ZIMPL support)\n2. Downloads SCIP Optimization Suite with ZIMPL\n3. Compiles with Emscripten to WebAssembly\n4. Bundles JavaScript wrapper\n\n## File Sizes\n\n| File | Size | Gzipped |\n|------|------|---------|\n| scip.wasm | ~8 MB | ~2.5 MB |\n| scip.js | ~60 KB | ~18 KB |\n\n## Limitations\n\n- **Single-threaded**: WASM runs single-threaded (use Web Workers for parallelism at application level)\n- **Memory**: Limited to ~2GB (WASM 32-bit limit)\n- **Callbacks in workers**: Function callbacks are not transferable to Web Workers\n\n## Performance\n\nTypical solving times in browser (varies by problem complexity):\n\n| Problem Type | Variables | Constraints | Time |\n|--------------|-----------|-------------|------|\n| LP | 1,000 | 500 | <1s |\n| LP | 10,000 | 5,000 | ~5s |\n| MIP | 100 binary | 50 | ~1s |\n| MIP | 1,000 binary | 500 | ~30s |\n| MINLP (quadratic) | 50 | 20 | ~2s |\n| MINLP (polynomial) | 100 | 50 | ~10s |\n\n## License\n\nApache 2.0 (same as SCIP)\n\n## Credits\n\n- [SCIP Optimization Suite](https://scipopt.org) - The underlying solver\n- [ZIMPL](https://zimpl.zib.de/) - Modeling language for MINLP\n- [Emscripten](https://emscripten.org) - C++ to WebAssembly compiler\n\n## Links\n\n- [SCIP Documentation](https://scipopt.org/doc/html/)\n- [ZIMPL User Guide](https://zimpl.zib.de/download/zimpl.pdf)\n- [LP Format Reference](https://www.gurobi.com/documentation/current/refman/lp_format.html)\n","readmeFilename":"README.md"}