mostly reindent of examples

examples/java/FlowExample.java

@@ -14,9 +14,12 @@
 import com.google.ortools.graph.MaxFlow;
 import com.google.ortools.graph.MinCostFlow;
 
-/** Sample showing how to model using the flow solver. */
-public class FlowExample {
+/**
+ * Sample showing how to model using the flow solver.
+ *
+ */
 
+public class FlowExample {
   static {
     System.loadLibrary("jniortools");
   }
@@ -26,11 +29,7 @@ public class FlowExample {
     final int numSources = 4;
     final int numTargets = 4;
     final int[][] costs = {
-      {90, 75, 75, 80},
-      {35, 85, 55, 65},
-      {125, 95, 90, 105},
-      {45, 110, 95, 115}
-    };
+        {90, 75, 75, 80}, {35, 85, 55, 65}, {125, 95, 90, 105}, {45, 110, 95, 115}};
     final int expectedCost = 275;
     MinCostFlow minCostFlow = new MinCostFlow();
     for (int source = 0; source < numSources; ++source) {
@@ -48,13 +47,8 @@ public class FlowExample {
       System.out.println("total flow = " + totalFlowCost + "/" + expectedCost);
       for (int i = 0; i < minCostFlow.getNumArcs(); ++i) {
         if (minCostFlow.getFlow(i) > 0) {
-          System.out.println(
-              "From source "
-                  + minCostFlow.getTail(i)
-                  + " to target "
-                  + minCostFlow.getHead(i)
-                  + ": cost "
-                  + minCostFlow.getUnitCost(i));
+          System.out.println("From source " + minCostFlow.getTail(i) + " to target "
+              + minCostFlow.getHead(i) + ": cost " + minCostFlow.getUnitCost(i));
         }
       }
     } else {
@@ -75,15 +69,8 @@ public class FlowExample {
     if (maxFlow.solve(0, 5) == MaxFlow.Status.OPTIMAL) {
       System.out.println("Total flow " + maxFlow.getOptimalFlow() + "/" + expectedTotalFlow);
       for (int i = 0; i < maxFlow.getNumArcs(); ++i) {
-        System.out.println(
-            "From source "
-                + maxFlow.getTail(i)
-                + " to target "
-                + maxFlow.getHead(i)
-                + ": "
-                + maxFlow.getFlow(i)
-                + " / "
-                + maxFlow.getCapacity(i));
+        System.out.println("From source " + maxFlow.getTail(i) + " to target " + maxFlow.getHead(i)
+            + ": " + maxFlow.getFlow(i) + " / " + maxFlow.getCapacity(i));
       }
       // TODO(user): Our SWIG configuration does not currently handle these
       // functions correctly in Java:

examples/java/IntegerProgramming.java

@@ -16,7 +16,11 @@ import com.google.ortools.linearsolver.MPObjective;
 import com.google.ortools.linearsolver.MPSolver;
 import com.google.ortools.linearsolver.MPVariable;
 
-/** Integer programming example that shows how to use the API. */
+/**
+ * Integer programming example that shows how to use the API.
+ *
+ */
+
 public class IntegerProgramming {
   static {
     System.loadLibrary("jniortools");
@@ -27,7 +31,6 @@ public class IntegerProgramming {
       return new MPSolver(
           "IntegerProgrammingExample", MPSolver.OptimizationProblemType.valueOf(solverType));
     } catch (java.lang.IllegalArgumentException e) {
-      System.err.println("Bad solver type: " + e);
       return null;
     }
   }
@@ -63,10 +66,9 @@ public class IntegerProgramming {
 
     // Verify that the solution satisfies all constraints (when using solvers
     // others than GLOP_LINEAR_PROGRAMMING, this is highly recommended!).
-    if (!solver.verifySolution(/*tolerance=*/ 1e-7, /*logErrors=*/ true)) {
-      System.err.println(
-          "The solution returned by the solver violated the"
-              + " problem constraints by at least 1e-7");
+    if (!solver.verifySolution(/*tolerance=*/1e-7, /* log_errors= */ true)) {
+      System.err.println("The solution returned by the solver violated the"
+          + " problem constraints by at least 1e-7");
       return;
     }
 

examples/java/Knapsack.java

@@ -13,35 +13,26 @@
 
 import com.google.ortools.algorithms.KnapsackSolver;
 
-/** Sample showing how to model using the knapsack solver. */
+/**
+ * Sample showing how to model using the knapsack solver.
+ *
+ */
+
 public class Knapsack {
   static {
     System.loadLibrary("jniortools");
   }
 
   private static void solve() {
-    KnapsackSolver solver =
-        new KnapsackSolver(
-            KnapsackSolver.SolverType.KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER, "test");
-    final long[] profits = {
-      360, 83, 59, 130, 431, 67, 230, 52, 93,
-      125, 670, 892, 600, 38, 48, 147, 78, 256,
-      63, 17, 120, 164, 432, 35, 92, 110, 22,
-      42, 50, 323, 514, 28, 87, 73, 78, 15,
-      26, 78, 210, 36, 85, 189, 274, 43, 33,
-      10, 19, 389, 276, 312
-    };
+    KnapsackSolver solver = new KnapsackSolver(
+        KnapsackSolver.SolverType.KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER, "test");
+    final long[] profits = {360, 83, 59, 130, 431, 67, 230, 52, 93, 125, 670, 892, 600, 38, 48, 147,
+        78, 256, 63, 17, 120, 164, 432, 35, 92, 110, 22, 42, 50, 323, 514, 28, 87, 73, 78, 15, 26,
+        78, 210, 36, 85, 189, 274, 43, 33, 10, 19, 389, 276, 312};
 
-    final long[][] weights = {
-      {
-        7, 0, 30, 22, 80, 94, 11, 81, 70,
-        64, 59, 18, 0, 36, 3, 8, 15, 42,
-        9, 0, 42, 47, 52, 32, 26, 48, 55,
-        6, 29, 84, 2, 4, 18, 56, 7, 29,
-        93, 44, 71, 3, 86, 66, 31, 65, 0,
-        79, 20, 65, 52, 13
-      }
-    };
+    final long[][] weights = {{7, 0, 30, 22, 80, 94, 11, 81, 70, 64, 59, 18, 0, 36, 3, 8, 15, 42, 9,
+        0, 42, 47, 52, 32, 26, 48, 55, 6, 29, 84, 2, 4, 18, 56, 7, 29, 93, 44, 71, 3, 86, 66, 31,
+        65, 0, 79, 20, 65, 52, 13}};
 
     final long[] capacities = {850};
 

examples/java/LinearAssignmentAPI.java

@@ -15,25 +15,21 @@ import com.google.ortools.graph.LinearSumAssignment;
 
 /**
  * Test assignment on a 4x4 matrix. Example taken from
- * http://www.ee.oulu.fi/~mpa/matreng/eem1_2-1.htm with kCost[0][1] modified so the optimum solution
- * is unique.
+ *  http://www.ee.oulu.fi/~mpa/matreng/eem1_2-1.htm with kCost[0][1]
+ *  modified so the optimum solution is unique.
+ *
  */
-public class LinearAssignmentAPI {
 
+public class LinearAssignmentAPI {
   static {
     System.loadLibrary("jniortools");
   }
 
-
   private static void runAssignmentOn4x4Matrix() {
     final int numSources = 4;
     final int numTargets = 4;
     final int[][] cost = {
-      {90, 76, 75, 80},
-      {35, 85, 55, 65},
-      {125, 95, 90, 105},
-      {45, 110, 95, 115}
-    };
+        {90, 76, 75, 80}, {35, 85, 55, 65}, {125, 95, 90, 105}, {45, 110, 95, 115}};
     final int expectedCost = cost[0][3] + cost[1][2] + cost[2][1] + cost[3][0];
 
     LinearSumAssignment assignment = new LinearSumAssignment();
@@ -46,13 +42,8 @@ public class LinearAssignmentAPI {
     if (assignment.solve() == LinearSumAssignment.Status.OPTIMAL) {
       System.out.println("Total cost = " + assignment.getOptimalCost() + "/" + expectedCost);
       for (int node = 0; node < assignment.getNumNodes(); ++node) {
-        System.out.println(
-            "Left node "
-                + node
-                + " assigned to right node "
-                + assignment.getRightMate(node)
-                + " with cost "
-                + assignment.getAssignmentCost(node));
+        System.out.println("Left node " + node + " assigned to right node "
+            + assignment.getRightMate(node) + " with cost " + assignment.getAssignmentCost(node));
       }
     } else {
       System.out.println("No solution found.");

examples/java/LinearProgramming.java

@@ -12,12 +12,15 @@
 // limitations under the License.
 
 import com.google.ortools.linearsolver.MPConstraint;
-import com.google.ortools.linearsolver.MPModelExportOptions;
 import com.google.ortools.linearsolver.MPObjective;
 import com.google.ortools.linearsolver.MPSolver;
 import com.google.ortools.linearsolver.MPVariable;
 
-/** Linear programming example that shows how to use the API. */
+/**
+ * Linear programming example that shows how to use the API.
+ *
+ */
+
 public class LinearProgramming {
   static {
     System.loadLibrary("jniortools");
@@ -73,8 +76,7 @@ public class LinearProgramming {
     System.out.println("Number of constraints = " + solver.numConstraints());
 
     if (printModel) {
-      MPModelExportOptions options = new MPModelExportOptions();
-      String model = solver.exportModelAsLpFormat(options);
+      String model = solver.exportModelAsLpFormat();
       System.out.println(model);
     }
 
@@ -88,10 +90,9 @@ public class LinearProgramming {
 
     // Verify that the solution satisfies all constraints (when using solvers
     // others than GLOP_LINEAR_PROGRAMMING, this is highly recommended!).
-    if (!solver.verifySolution(/*tolerance=*/ 1e-7, /*logErrors=*/ true)) {
-      System.err.println(
-          "The solution returned by the solver violated the"
-              + " problem constraints by at least 1e-7");
+    if (!solver.verifySolution(/*tolerance=*/1e-7, /* log_errors= */ true)) {
+      System.err.println("The solution returned by the solver violated the"
+          + " problem constraints by at least 1e-7");
       return;
     }
 

examples/java/RabbitsPheasants.java

@@ -16,7 +16,10 @@ import com.google.ortools.constraintsolver.IntVar;
 import com.google.ortools.constraintsolver.Solver;
 import java.util.logging.Logger;
 
-/** Sample showing how to model using the constraint programming solver. */
+/**
+ * Sample showing how to model using the constraint programming solver.
+ *
+ */
 public class RabbitsPheasants {
   private static Logger logger = Logger.getLogger(RabbitsPheasants.class.getName());
 
@@ -25,8 +28,9 @@ public class RabbitsPheasants {
   }
 
   /**
-   * Solves the rabbits + pheasants problem. We are seing 20 heads and 56 legs. How many rabbits and
-   * how many pheasants are we thus seeing?
+   * Solves the rabbits + pheasants problem.  We are seing 20 heads
+   * and 56 legs. How many rabbits and how many pheasants are we thus
+   * seeing?
    */
   private static void solve(boolean traceSearch) {
     ConstraintSolverParameters parameters = ConstraintSolverParameters.newBuilder()