support quadratic programs

README.md

@@ -166,6 +166,18 @@ console.log(`Objective value: ${model.ObjVal}`); // 17
 console.log(`Included items: ${itemNames.filter(name => included[name].value > 0.5)}`); // A,B,D
 ```
 
+### Example 3: quadratic objective function
+
+The HiGHS solver supports (convex) quadratic objective functions. Quadratic terms are specified as length-3 arrays, with coefficient followed by the two variables that are being multiplied (which may be the same variable in case of a squared term). Here is an example of how to model a quadratic objective function.
+
+```javascript
+const x = model.addVar({ name: "x" });
+model.addConstr([x], ">=", 10);
+model.setObjective([[3, x, x]], "MINIMIZE"); // minimize 3 x^2
+await model.solve(highs);
+console.log(`Objective value: ${model.ObjVal}, with x = ${x.value}`); // 300, with x = 10
+```
+
 ## API
 
 <a name="new_module_lp-model.Model_new"></a>

src/model.js

@@ -325,6 +325,9 @@ export class Model {
      * @see {@link https://www.npmjs.com/package/jsLPSolver}
      */
     toJSLPSolverFormat(options) {
+        if (this.isQuadratic()) {
+            throw new Error("jsLPSolver does not support quadratic models.");
+        }
         return toJSLPSolverFormat(this, options);
     }
 

src/write-lp-format.js

@@ -7,7 +7,7 @@ export function toLPFormat(model) {
                 if (term.length === 2) {
                     return `${term[0]} ${term[1].name}`;
                 } else if (term.length === 3) {
-                    return `[${term[0]} ${term[1].name} * ${term[2].name}]`;
+                    return `[ ${term[0]*2} ${term[1].name} * ${term[2].name} ]/2`;
                 }
             } else {
                 return `${term}`;

test/test.js

@@ -42,21 +42,21 @@ async function testInfeasible() {
     console.log(m.status);
 }
 
-// async function testQuadratic() {
-//     const LPModel = require('../dist/lp-model.js');
-//     const m = new LPModel.Model();
-//     const x = m.addVar({ name: "x" });
-//     const y = m.addVar({ name: "y" });
-//     m.setObjective([[1, x, x]], "MINIMIZE");
-//     m.addConstr([x, y], ">=", 1);
-
-//     console.log(m.toLPFormat());
-
-//     const highs = await require("highs")();
-//     m.solve(highs);
-//     console.log(x.value, y.value);
-// }
-// testQuadratic();
+async function testQuadratic() {
+    const LPModel = require('../dist/lp-model.js');
+    const m = new LPModel.Model();
+    const x = m.addVar({ name: "x" });
+    m.setObjective([[1, x, x]], "MINIMIZE");
+    m.addConstr([x], ">=", 10);
+    m.addConstr([x], "<=", 20);
+
+    console.log(m.toLPFormat());
+
+    const highs = await require("highs")();
+    m.solve(highs);
+    console.log(x.value, x.value === 10);
+    console.log(m.ObjVal, m.ObjVal === 100);
+}
 
 async function testObjectiveConstantTerm() {
     const LPModel = require('../dist/lp-model.js');
@@ -82,5 +82,6 @@ async function allTests() {
     await test1();
     await testInfeasible();
     await testObjectiveConstantTerm();
+    await testQuadratic();
 }
 allTests();