readme and demo updates

README.md

@@ -6,7 +6,12 @@
 
 JavaScript package for modelling (Integer) Linear Programs
 
-This is a lightweight JS package for specifying LPs and ILPs using a convenient syntax. The constructed model can be exported to the `.lp` [CPLEX LP format](https://web.mit.edu/lpsolve/doc/CPLEX-format.htm), and solved using the [highs-js](https://github.com/lovasoa/highs-js) and [glpk.js](https://github.com/jvail/glpk.js) solvers available as WebAssembly. This can be done both in the browser ([demo page](https://dominikpeters.github.io/lp-model/)) and in Node.js.
+This is a lightweight JS package for specifying LPs and ILPs using a convenient syntax. The constructed model can be exported to the `.lp` [CPLEX LP format](https://web.mit.edu/lpsolve/doc/CPLEX-format.htm), and solved using 
+* [highs-js](https://github.com/lovasoa/highs-js) (WebAssembly wrapper for the [HiGHS solver](https://github.com/ERGO-Code/HiGHS), a high-performance LP/ILP solver),
+* [glpk.js](https://github.com/jvail/glpk.js) (WebAssembly wrapper for the [GLPK solver](https://www.gnu.org/software/glpk/)), and
+* [jsLPSolver](https://github.com/JWally/jsLPSolver) (a pure JS solver, not as fast as the others, but small bundle size).
+
+All solvers work both in the browser ([demo page](https://dominikpeters.github.io/lp-model/)) and in Node.js.
 
 ## Installation
 
@@ -17,6 +22,7 @@ npm install lp-model
 # optionally install the solvers
 npm install highs
 npm install glpk.js
+npm install javascript-lp-solver
 ```
 
 In the browser:
@@ -42,21 +48,28 @@ async function main() {
     // or
     const glpk = await require("glpk.js")();
     model.solve(glpk);
+    // or
+    const jsLPSolver = require("javascript-lp-solver");
+    model.solve(jsLPSolver);
 }
 main();
 ```
 
-Setup in the browser for high-js:
+Setup in the browser for high-js and jsLPSolver:
 
 ```html
 <script src="https://cdn.jsdelivr.net/npm/lp-model@latest/dist/lp-model.min.js"></script>
 <script src="https://cdn.jsdelivr.net/npm/highs/build/highs.js"></script>
+<script src="https://unpkg.com/javascript-lp-solver/prod/solver.js"></script>
 <script>
     async function main() {
         const model = new LPModel.Model();
         // ...
         const highs = await Module();
         model.solve(highs);
+        // or
+        const jsLPSolver = window.solver;
+        model.solve(jsLPSolver);
     }
     main();
 </script>
@@ -110,17 +123,98 @@ const problem = {
 
 const itemNames = problem.items.map(item => item.name);
 
+// make binary variables for each item
 const included = model.addVars(itemNames, { vtype: "BINARY" });
 // included[A], included[B], included[C], included[D] are binary variables
 
-model.addConstr(problem.items.map((item, i) => [item.weight, included[item.name]]), "<=", problem.capacity);
 // sum of weights of included items <= capacity
+model.addConstr(problem.items.map((item, i) => [item.weight, included[item.name]]), "<=", problem.capacity);
 // equivalent to: 3*included[A] + 4*included[B] + 5*included[C] + 8*included[D] <= 15
 
-model.setObjective(problem.items.map((item, i) => [item.value, included[item.name]]), "MAXIMIZE");
 // maximize sum of values of included items
+model.setObjective(
+    problem.items.map((item, i) => [item.value, included[item.name]]), 
+    "MAXIMIZE"
+    );
 
 await model.solve(highs);
-console.log(`Objective value: ${model.ObjVal}`);
+console.log(`Objective value: ${model.ObjVal}`); // 17
 console.log(`Included items: ${itemNames.filter(name => included[name].value > 0.5)}`); // A,B,D
-```
+```
+
+## API
+
+### model.addVar(options) ⇒ <code>Var</code>
+Adds a variable to the model.
+
+**Kind**: instance method of [<code>Model</code>](#module_lp-model.Model)  
+**Returns**: <code>Var</code> - The created variable instance.  
+
+
+| Param | Type | Default | Description |
+| --- | --- | --- | --- |
+| options | <code>Object</code> |  | Options for creating the variable. |
+| [options.lb] | <code>number</code> \| <code>&quot;-infinity&quot;</code> | <code>0</code> | The lower bound of the variable. |
+| [options.ub] | <code>number</code> \| <code>&quot;+infinity&quot;</code> | <code>&quot;+infinity&quot;</code> | The upper bound of the variable. |
+| [options.vtype] | <code>&quot;CONTINUOUS&quot;</code> \| <code>&quot;BINARY&quot;</code> \| <code>&quot;INTEGER&quot;</code> | <code>&quot;CONTINUOUS&quot;</code> | The type of the variable. |
+| [options.name] | <code>string</code> |  | The name of the variable. If not provided, a unique name is generated. |
+
+<a name="module_lp-model.Model+addVars"></a>
+
+### model.addVars(varNames, options) ⇒ <code>Object</code>
+Adds multiple variables to the model based on an array of names.
+Each variable is created with the same provided options.
+
+**Returns**: <code>Object</code> - An object where keys are variable names and values are the created variable instances.  
+
+
+| Param | Type | Default | Description |
+| --- | --- | --- | --- |
+| varNames | <code>Array.&lt;string&gt;</code> |  | Array of names for the variables to be added. |
+| options | <code>Object</code> |  | Common options for creating the variables. |
+| [options.lb] | <code>number</code> \| <code>&quot;-infinity&quot;</code> | <code>0</code> | The lower bound for all variables. |
+| [options.ub] | <code>number</code> \| <code>&quot;+infinity&quot;</code> | <code>&quot;+infinity&quot;</code> | The upper bound for all variables. |
+| [options.vtype] | <code>&quot;CONTINUOUS&quot;</code> \| <code>&quot;BINARY&quot;</code> \| <code>&quot;INTEGER&quot;</code> | <code>&quot;CONTINUOUS&quot;</code> | The type for all variables. |
+
+<a name="module_lp-model.Model+setObjective"></a>
+
+### model.setObjective(expression, sense)
+Sets the objective function of the model.
+
+| Param | Type | Description |
+| --- | --- | --- |
+| expression | <code>Array</code> | The linear expression representing the objective function. |
+| sense | <code>&quot;MAXIMIZE&quot;</code> \| <code>&quot;MINIMIZE&quot;</code> | The sense of optimization, either "MAXIMIZE" or "MINIMIZE". |
+
+<a name="module_lp-model.Model+addConstr"></a>
+
+### model.addConstr(lhs, comparison, rhs) ⇒ <code>Constr</code>
+Adds a constraint to the model.
+
+**Returns**: <code>Constr</code> - The created constraint instance.  
+
+| Param | Type | Description |
+| --- | --- | --- |
+| lhs | <code>Array</code> | The left-hand side expression of the constraint. |
+| comparison | <code>string</code> | The comparison operator, either "<=", "=", or ">=". |
+| rhs | <code>number</code> \| <code>Array</code> | The right-hand side, which can be a number or a linear expression. |
+
+<a name="module_lp-model.Model+toLPFormat"></a>
+
+### model.toLPFormat() ⇒ <code>string</code>
+Converts the model to CPLEX LP format string.
+
+**Returns**: <code>string</code> - The model represented in LP format.  
+**See**: [https://web.mit.edu/lpsolve/doc/CPLEX-format.htm](https://web.mit.edu/lpsolve/doc/CPLEX-format.htm)  
+
+<a name="module_lp-model.Model+solve"></a>
+
+### model.solve(solver, [options])
+Solves the model using the provided solver. highs-js, glpk.js, or jsLPSolver can be used. 
+The solution can be accessed from the variables' `value` properties and the constraints' `primal` and `dual` properties.
+
+| Param | Type | Default | Description |
+| --- | --- | --- | --- |
+| solver | <code>Object</code> |  | The solver instance to use for solving the model, either from highs-js, glpk.js, or jsLPSolver. |
+| [options] | <code>Object</code> | <code>{}</code> | Options to pass to the solver's solve method (refer to their respective documentation: https://ergo-code.github.io/HiGHS/dev/options/definitions/, https://www.npmjs.com/package/glpk.js, https://github.com/JWally/jsLPSolver?tab=readme-ov-file#options). |
+

index.html

@@ -18,11 +18,12 @@ <h1>lp-model</h1>
             convenient syntax, used together with the <code>highs-js</code> and <code>glpk.js</code> solvers using
             WebAssembly.
         </p>
-        <pre id="output"></pre>
+        <pre id="output" style="font-size: 0.85rem;"></pre>
         <p>If you see an ILP written in the CPLEX LP format, and both the HiGHS solution and the GLPK solution, it worked! <a href="https://github.com/DominikPeters/lp-model/blob/master/index.html">HTML source code</a></p>
     </main>
     <!-- <script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/lp-model.min.js"></script> -->
     <script src="https://cdn.jsdelivr.net/npm/highs/build/highs.js"></script>
+    <script src="https://unpkg.com/javascript-lp-solver/prod/solver.js"></script>
     <script type="module">
         import { Model } from "https://cdn.jsdelivr.net/npm/[email protected]/dist/lp-model.es.min.js";
         import GLPK from "https://cdn.jsdelivr.net/npm/glpk.js";
@@ -49,6 +50,10 @@ <h1>lp-model</h1>
             const glpk = await GLPK();
             await m.solve(glpk);
             pre.textContent += `\n\nGLPK solution:\n x = ${x.value}\n y = ${y.value}`;
+
+            const jsLPSolver = window.solver;
+            await m.solve(jsLPSolver);
+            pre.textContent += `\n\njsLPSolver solution:\n x = ${x.value}\n y = ${y.value}`;
         }
 
         window.main = main;

src/model.js

@@ -1,3 +1,8 @@
+/**
+ * A module for specifying LPs and ILPs using a convenient syntax, and solving them with various solvers.
+ * @module lp-model
+ */
+
 import { toGLPKFormat, readGLPKSolution } from './glpk-js-bridge.js';
 import { toJSLPSolverFormat, readJSLPSolverSolution } from './jsLPSolver-bridge.js';
 import { readHighsSolution } from './highs-js-bridge.js';