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LOJKINE"},"license":"MIT","homepage":"https://lovasoa.github.io/highs-js/","keywords":["linear programming","LP","MILP","solver","maths","or","optimization"],"repository":{"url":"git+https://github.com/lovasoa/highs-js.git","type":"git"},"description":"Mixed integer linear programming library, built by compiling a high-performance C++ solver developed by the University of Edinburgh (HiGHS) to WebAssembly.","maintainers":[{"name":"lovasoa","email":"pere.jobs@gmail.com"}],"readme":"# highs-js\n\n[![npm version](https://badge.fury.io/js/highs.svg)](https://www.npmjs.com/package/highs)\n[![CI status](https://github.com/lovasoa/highs-js/actions/workflows/CI.yml/badge.svg)](https://github.com/lovasoa/highs-js/actions/workflows/CI.yml)\n[![package size](https://badgen.net/bundlephobia/minzip/highs)](https://bundlephobia.com/result?p=highs)\n\nUse the [HiGHS](https://highs.dev/) optimization solver from JavaScript.\n`highs-js` compiles HiGHS to WebAssembly and solves linear, mixed-integer, and\nconvex quadratic models in Node.js and modern browsers. Everything runs\nlocally. There is no native add-on and no solver service to operate.\n\n```sh\nnpm install highs\n```\n\nThe package supports CommonJS and native ES modules, includes TypeScript\ndeclarations, and offers a small one-shot API alongside a persistent API for\nlarger applications.\n\n## What can it solve?\n\nOptimization finds the best decision that still satisfies every constraint.\nThe decision might be how much to produce, which jobs to schedule, where to\nopen facilities, or how to balance return against risk.\n\n| Model | Best suited to | Examples |\n| --- | --- | --- |\n| **LP (linear programming)** | Continuous decisions with linear costs and constraints | Blending, transportation, energy dispatch, production planning |\n| **MILP/MIP (mixed-integer linear programming)** | Whole-number or yes/no decisions with linear costs and constraints | Scheduling, assignment, packing, facility location, routing models |\n| **Convex QP (quadratic programming)** | Continuous decisions with a convex quadratic objective | Portfolio risk, constrained least squares, control, smooth resource allocation |\n\nHiGHS represents these models as\n\n```text\nminimize or maximize    1/2 xᵀQx + cᵀx\nsubject to              rowLower ≤ Ax ≤ rowUpper\n                        colLower ≤  x ≤ colUpper\n```\n\nWith `Q = 0`, continuous variables give an LP and integer variables give a MIP.\nFor a convex QP, `Q` is positive semidefinite when minimizing and negative\nsemidefinite when maximizing.\n\nHiGHS is a linear and convex quadratic solver. It does not handle general\nnonlinear models or mixed-integer quadratic programs.\n\n## Quick start\n\nA workshop makes chairs and tables. Each product earns a profit and uses\ncarpentry and finishing time, both in limited supply. Chairs and tables must be\nproduced in whole units, so the model is a MIP.\n\n```js\nimport loadHighs from \"highs\";\n\nconst highs = await loadHighs();\n\nconst problem = `Maximize\n   profit: 30 chairs + 50 tables\n  Subject To\n   carpentry: chairs + 2 tables <= 40\n   finishing: 2 chairs + tables <= 50\n  Bounds\n   chairs >= 0\n   tables >= 0\n  Generals\n   chairs tables\n  End`;\n\nconst result = highs.solve(problem, { output_flag: false });\n\nif (result.Status !== \"Optimal\") {\n  throw new Error(`Solve ended with status: ${result.Status}`);\n}\n\nconsole.log(result.ObjectiveValue);         // 1100\nconsole.log(result.Columns.chairs.Primal);  // 20\nconsole.log(result.Columns.tables.Primal);  // 10\n```\n\nThe optimum is 20 chairs and 10 tables, for a profit of 1100. Remove the\n`Generals` section to allow fractional quantities and solve the model as an LP.\n\nCommonJS uses the same loader:\n\n```js\nconst loadHighs = require(\"highs\");\n```\n\n## Choose an API\n\nMost programs need one of these three entry points.\n\n| Task | API |\n| --- | --- |\n| Solve one model already written in CPLEX LP format | `highs.solve(lpText, options)` |\n| Keep a model in memory, modify it, and solve it again | [`highs.createModel(...)`](#persistent-models) |\n| Call the stable HiGHS C API directly and retain numeric status codes | `highs.raw` |\n\n`highs.solve()` is the original compatibility API. It accepts CPLEX LP text and\nreturns results keyed by variable name.\n\nA persistent `Model` owns one HiGHS instance until it is disposed. JavaScript\ngarbage collection does not release that Wasm allocation; see\n[model lifetime](#model-lifetime) before using `highs.createModel()`. Use it for\nstructured sparse input, repeated solves, model changes, callbacks, bases,\nranging, IIS analysis, and other advanced features.\n\n## Algorithms\n\nHiGHS chooses a suitable algorithm when `solver` is set to its default value,\n`\"choose\"`. The JavaScript API exposes the main controls and diagnostics used\nby the native solver.\n\n| Model | Algorithms available | Main controls |\n| --- | --- | --- |\n| **LP** | Primal and dual revised simplex, an interior-point method with optional crossover, and PDLP | `solver`, `simplex_strategy`, edge-weight strategies, `run_crossover`, tolerances, iteration limits |\n| **MIP** | Branch-and-cut with presolve, LP relaxations, cutting planes, heuristics, restarts, and symmetry detection | Gap limits, node limits, solution limits, feasibility settings, heuristic effort, symmetry, restarts, callbacks |\n| **Convex QP** | Primal active-set QP solver | `qp_iteration_limit`, `qp_nullspace_limit`, sparse Hessian input |\n\nFor LP models, `solver` accepts:\n\n- `\"choose\"`: let HiGHS choose\n- `\"simplex\"`: revised simplex\n- `\"ipm\"`: interior-point method\n- `\"pdlp\"`: a first-order method based on primal-dual hybrid gradient\n\n`simplex_strategy` selects automatic mode, serial dual simplex, the PAMI and SIP\ndual variants, or primal simplex. The edge-weight options include Dantzig,\nDevex, and steepest edge where the selected algorithm supports them.\n\nFor MIP and QP models, leave `solver` at `\"choose\"` unless you explicitly want\nthe LP relaxation. Selecting `\"simplex\"`, `\"ipm\"`, or `\"pdlp\"` tells HiGHS to\nignore integrality and the quadratic term.\n\nThis WebAssembly build is single-threaded. Run independent solves in Web\nWorkers when an application needs parallelism or must keep a browser interface\nresponsive.\n\nThe upstream [HiGHS solver overview](https://ergo-code.github.io/HiGHS/dev/solvers/)\ndescribes the algorithms in more depth.\n\n## Persistent models\n\nCreate a model from LP text, MPS bytes, sparse arrays, or an empty instance. The\nfollowing example keeps the production model in memory, changes one bound, and\nsolves it again without reparsing the LP text.\n\n### Model lifetime\n\nEvery model created by `highs.createModel()` owns native memory inside the Wasm\nruntime. Garbage collection does not call `Highs_destroy`, so abandoning a model\nwithout disposing it leaks that allocation. Repeated leaks can make Wasm memory\ngrow for the rest of the runtime's lifetime.\n\nPrefer `withModel()` when the model only needs a lexical operation. It disposes\nafter a synchronous callback returns or an asynchronous callback settles:\n\n```js\nconst objective = highs.withModel(\n  { format: \"lp\", data: problem },\n  (model) => {\n    model.run();\n    return model.getObjectiveValue();\n  },\n);\n```\n\nTypeScript projects using `ESNext.Disposable` can use `using` when the runtime\nprovides `Symbol.dispose` (or a polyfill installs it before model creation):\n\n```ts\nusing model = highs.createModel({ format: \"lp\", data: problem });\nmodel.run();\n```\n\nFor long-lived models and runtimes without `Symbol.dispose`, call the idempotent\n`model.dispose()` in `finally`. Do not retain a model passed to `withModel()`;\nit is disposed when the callback completes. Detached strings and typed arrays\nreturned before disposal remain usable. Any later operation through a stale\n`model`, `model.raw`, `model.options`, or `model.info` reference throws a clear\n`HighsDisposedError` in JavaScript before the freed Wasm allocation is accessed.\n\n```js\nconst model = highs.createModel({ format: \"lp\", data: problem });\n\ntry {\n  model.options.set({ output_flag: false, presolve: \"on\" });\n\n  model.run();\n  console.log(model.getObjectiveValue());\n\n  const tables = model.getColByName(\"tables\");\n  model.changeColBounds(tables, 0, 6);\n  model.run();\n\n  console.log(model.getSolution().colValue);\n} finally {\n  model.dispose();\n}\n```\n\nInputs are validated before they enter WebAssembly. Arrays returned by the\npublic API are JavaScript-owned copies, so a later solver call or `dispose()`\ndoes not invalidate them.\n\nYou can also build a model incrementally:\n\n```js\nconst model = highs.createModel();\nmodel.addVar(0, highs.infinity);\nmodel.changeColCost(0, 12);\nmodel.changeColIntegrality(0, highs.constants.variableType.integer);\nmodel.addRow(-highs.infinity, 20, { indices: [0], values: [3] });\n```\n\nFor large application-generated models, pass CSC or CSR arrays directly to\n`highs.createModel(modelData)`. Add `integrality` for a MIP or `hessian` for a\nconvex QP.\n\n## API map\n\nThe table below is a compact index to the persistent API. See the\n[generated API reference](https://lovasoa.github.io/highs-js/docs/) or\n[`types.d.ts`](./types.d.ts) for complete signatures.\n\n| Area | Representative API |\n| --- | --- |\n| Create and replace models | `passModel`, `readModel`, `addVar(s)`, `addRow(s)`, `addCol(s)` |\n| Change models | `changeColBounds`, `changeRowBounds`, `changeColCost`, `changeCoefficient`, `changeColIntegrality`, `deleteRows`, `deleteCols`, `scaleRow`, `scaleCol` |\n| Manage the solve lifecycle | `presolve`, `run`, `postsolve`, `clearSolver`, `clearModel`, `releaseMemory`, `dispose` |\n| Read and seed solutions | `getModelStatus`, `getObjectiveValue`, `getSolution`, `setSolution`, `getPrimalRay`, `getDualRay` |\n| Work with simplex bases | `getBasis`, `setBasis`, `setLogicalBasis`, `getBasicVariables`, basis inverse and solve methods, reduced rows and columns, `crossover` |\n| Diagnose infeasibility and sensitivity | `getIis`, `getIisLp`, `feasibilityRelaxation`, `getRanging`, `getFixedLp` |\n| Inspect model data | `getModel`, `getLp`, `getPresolvedLp`, `getCols`, `getRows`, name lookups |\n| Import and export | LP text, MPS bytes, presolved models, human-readable and machine-readable solutions |\n| Set multiple objectives | `passLinearObjectives`, `addLinearObjective`, `clearLinearObjectives` |\n| Configure the solver | `model.options.get/set/describe/names/reset/read/export` |\n| Read solver statistics | `model.info.get/type`, `getRunTime`, `zeroAllClocks` |\n| Receive callbacks | Logging, simplex/IPM/MIP interrupts, MIP solutions, improving solutions, cut pools, user solutions |\n| Keep native status handling | `highs.raw.lpCall`, `mipCall`, `qpCall`, `highs.raw.createModel()` |\n\n## Advanced use\n\n### Options and solver information\n\n```js\nmodel.options.set(\"time_limit\", 30);\nmodel.options.set({ presolve: \"on\", mip_rel_gap: 0.01 });\n\nconsole.log(model.options.describe(\"mip_rel_gap\"));\nconsole.log(model.options.names());\n\nmodel.run();\nconsole.log(model.info.get(\"simplex_iteration_count\"));\nconsole.log(model.info.get(\"mip_node_count\")); // bigint\n```\n\nOption names match HiGHS and use `snake_case`. `describe()` reports an option's\ntype, current value, default, and numeric limits when available.\n\n### Convex QP Hessian\n\nA QP objective has the form `1/2 xᵀQx + cᵀx`. Pass `Q` as a sparse triangular\nor square Hessian.\n\n```js\nmodel.passHessian({\n  format: \"triangular\",\n  dimension: 2,\n  starts: new Int32Array([0, 1, 2]),\n  indices: new Int32Array([0, 1]),\n  values: new Float64Array([2, 4]),\n});\nmodel.run();\n```\n\nFor minimization, HiGHS expects a positive-semidefinite Hessian. For\nmaximization, it expects a negative-semidefinite Hessian.\n\n### Warm starts and bases\n\n```js\nconst basis = model.getBasis();\nconst solution = model.getSolution();\n\nconst next = highs.createModel(relatedModelData);\nnext.setBasis(basis);\nnext.setSolution({ colValue: solution.colValue });\nnext.run();\n```\n\nYou can warm-start a related LP from an existing simplex basis. The API also\nprovides basis inverse rows and columns, basis solves, transpose solves, and\nreduced tableau rows and columns.\n\n### Infeasibility analysis\n\n```js\nmodel.run();\n\nif (model.getModelStatus() === highs.constants.modelStatus.infeasible) {\n  const iis = model.getIis();\n  const conflictingModel = model.getIisLp();\n}\n```\n\nRelated tools include feasibility relaxation, primal and dual rays, ranging,\nfixed-LP extraction after a MIP solve, and inspection of the presolved model.\n\n### Callbacks\n\n```js\nmodel.run({\n  [highs.constants.callbackType.mipLogging](event) {\n    console.log(event.data.mip_node_count);\n  },\n  [highs.constants.callbackType.mipInterrupt](event) {\n    if (shouldStop()) event.interrupt();\n  },\n  [highs.constants.callbackType.mipUserSolution](event) {\n    event.setSolution({ indices: [0], values: [1] });\n  },\n});\n```\n\nCallbacks run synchronously. Returning a Promise throws\n`HighsValidationError`. Put long browser solves in a Worker to keep the main\nthread free.\n\n## Raw API\n\n`highs.raw` follows the stable, non-deprecated HiGHS C API while replacing\npointers with structured JavaScript values.\n\n```js\nconst result = highs.raw.lpCall(modelData);\n\nif (result.status !== highs.constants.status.error) {\n  console.log(result.value.modelStatus);\n  console.log(result.value.solution.colValue);\n}\n```\n\nUse `highs.raw.createModel()` for persistent C-style operations. Warnings and\nerrors remain numeric status values.\n\n## Model and solution I/O\n\n```js\nconst lpText = model.exportModel(\"lp\");\nconst mpsBytes = model.exportModel(\"mps\");\nconst solutionText = model.exportSolution(true);\n\nconst clone = highs.createModel({ format: \"lp\", data: lpText });\n```\n\nLP models use text; MPS models use bytes. The JavaScript API accepts and returns\ndata, so callers never need virtual filesystem paths.\n\n## WebAssembly loading\n\nNode.js normally finds `highs.wasm` beside the package loader. In a browser,\nserve the file as an asset and provide `locateFile` when it lives elsewhere.\n\n```js\nconst highs = await loadHighs({\n  locateFile: (file) => new URL(`/solver-assets/${file}`, location.href).href,\n});\n```\n\nThe loader also accepts `wasmBinary` or a precompiled `wasmModule`.\n\nLoading is asynchronous; solver calls are synchronous after initialization. A\nmodel cannot be used reentrantly. Call `dispose()` when a persistent model is no\nlonger needed.\n\n## Documentation and demos\n\n- [Extended API reference](https://lovasoa.github.io/highs-js/docs/)\n- [Online one-shot demo](https://lovasoa.github.io/highs-js/)\n- [Persistent API Worker demo](https://lovasoa.github.io/highs-js/extended/)\n- [Migration guide](https://lovasoa.github.io/highs-js/docs/migration/)\n- [JavaScript-to-C API mapping](https://lovasoa.github.io/highs-js/docs/c-api-mapping/)\n- [Canonical TypeScript declarations](./types.d.ts)\n- [Upstream HiGHS documentation](https://ergo-code.github.io/HiGHS/)\n\nThe `highs/legacy-types` compatibility entry points to the same `types.d.ts`\ndeclaration.\n\n## Versioning\n\nThe package major and minor version match the embedded HiGHS version. For\nexample, `highs-js` 1.14.x contains HiGHS 1.14.x. Patch releases may contain\nJavaScript API fixes without changing the solver version.\n","readmeFilename":"README.md"}