Merge pull request #18 from blubin/develop

Develop -> Master

pom.xml

@@ -4,7 +4,7 @@
 
     <groupId>edu.harvard.eecs</groupId>
     <artifactId>jopt</artifactId>
-    <version>1.3.3</version>
+    <version>1.3.4</version>
     <packaging>jar</packaging>
 
     <name>Java Optimization</name>
@@ -110,7 +110,7 @@
         <connection>scm:git:git://github.com/blubin/JOpt.git</connection>
         <developerConnection>scm:git:git@github.com/blubin/JOpt.git</developerConnection>
         <url>https://github.com/blubin/JOpt.git/tree/master/</url>
-        <tag>v1.3.3</tag>
+        <tag>v1.3.4</tag>
     </scm>
 
     <dependencies>
@@ -137,7 +137,7 @@
         <dependency>
             <groupId>cplex</groupId>
             <artifactId>cplex</artifactId>
-            <version>12.8</version>
+            <version>12.9</version>
             <scope>provided</scope>
             <optional>true</optional>
         </dependency>

src/main/java/edu/harvard/econcs/jopt/solver/IMIP.java

@@ -97,6 +97,9 @@ public interface IMIP extends Serializable {
 	 * of variables that distinguish different solutions in the context of the MIP. For example, in an auction,
 	 * these variables could be the allocation variables.
 	 *
+	 * If you're interested in a more advanced structure of variables of interest,
+	 * see {@link #setAdvancedVariablesOfInterest(Collection)}.
+	 *
 	 * @param variables The variables of interest. For SOLUTION_POOL_MODE = 3, only boolean variables are supported.
 	 */
 	default void setVariablesOfInterest(Collection<Variable> variables) {
@@ -109,9 +112,9 @@ default void setVariablesOfInterest(Collection<Variable> variables) {
 
 	/**
 	 * Internally, the variables of interest are stored as a collection of collections, even when the user
-     * sets them as a single collection. A user may, however, set the variables of interest directly in this advanced
+     * sets them as a single collection. But a user may want to set the variables of interest directly in this advanced
      * structure. This gives, without breaking the API of a simple collection of variables, the user the possibility
-	 * to define sets of variables that all have to be equal in their sum to be considered a duplicate.
+	 * to define <b>sets of variables that all have to be equal in their sum to be considered a duplicate.</b>
 	 *
 	 * @param variableSets The sets of variables of interest. For SOLUTION_POOL_MODE = 3, only boolean variables are
 	 *                     supported.

src/main/java/edu/harvard/econcs/jopt/solver/IMIPResult.java

@@ -45,27 +45,12 @@
  **/
 public interface IMIPResult extends Serializable, ISolution {
 
-
-
-
-
 	/** Returns the dual of a constraint that was added with IMIP.add(constraint, constraintId) */
 	double getDual(Constraint constraint);
-
-
+
 	/** Dump the results out to std out, using the MIP to make it pretty **/
 	String toString(IMIP mip);
 
 	/** Get the Queue of pool solutions, if available **/
 	Queue<PoolSolution> getPoolSolutions();
-
-	/**
-	 * @return the relative difference in the objective value compared to the relaxation of the problem
-	 */
-	double getRelativeGap();
-
-	/**
-	 * @return the absolute difference in the objective value compared to the relaxation of the problem
-	 */
-	double getAbsoluteGap();
 }

src/main/java/edu/harvard/econcs/jopt/solver/ISolution.java

@@ -66,6 +66,16 @@ default int compareTo(ISolution o) {
         return Double.compare(getObjectiveValue(), o.getObjectiveValue());
     }
 
+    /**
+     * @return the relative difference in the objective value compared to the relaxation of the problem
+     */
+    double getRelativeGap();
+
+    /**
+     * @return the absolute difference in the objective value compared to the relaxation of the problem
+     */
+    double getAbsoluteGap();
+
     /**
      * To take advantage of the advanced structure of variables of interest, this method checks if for all collections
      * collections of variables, the sum of these variables are equal.

src/main/java/edu/harvard/econcs/jopt/solver/SolveParam.java

@@ -233,6 +233,28 @@ public class SolveParam implements Serializable {
      * Enables data check by CPLEX. 1 = activated, 2 = activated and show debug information in log
      */
     public static final SolveParam DATACHECK = new SolveParam(24, Integer.class, "DataCheck");
+
+    /**
+     * Specifies type of optimality that CPLEX targets (optimal convex or first-order satisfaction) as it searches for a solution
+     * <ul>
+     *     <li>0 (default): Automatic - let CPLEX decide</li>
+     *     <li>1: Searches for a globally optimal solution to a convex model</li>
+     *     <li>2: Searches for a solution that satisfies first-order optimality conditions, but is not necessarily globally optimal</li>
+     *     <li>3: Searches for a globally optimal solution to a non-convex model; changes problem type to MIQP if necessary</li>
+     * </ul>
+     */
+    public static final SolveParam OPTIMALITY_TARGET = new SolveParam(25, Integer.class, "OptimalityTarget");
+
+    /**
+     * Switches on or off linearization of the quadratic terms in the objective function of a quadratic program (QP) or of a mixed integer quadratic program (MIQP) during preprocessing.
+     * <ul>
+     *     <li>-1 (default): Automatic - let CPLEX decide</li>
+     *     <li>0: Off: CPLEX does not linearize quadratic terms in the objective function of QP, MIQP</li>
+     *     <li>1: On: CPLEX linearizes quadratic terms in the objective function of QP, MIQP</li>
+     * </ul>
+     */
+    public static final SolveParam QTOLIN = new SolveParam(26, Integer.class, "QToLin");
+
     // Internal variables
     // ///////////////////
     /**
@@ -425,6 +447,10 @@ private Object readResolve() throws ObjectStreamException {
                 return SOLUTION_POOL_REPLACEMENT;
             case 24:
                 return DATACHECK;
+            case 25:
+                return OPTIMALITY_TARGET;
+            case 26:
+                return QTOLIN;
             case 101:
                 return PROBLEM_FILE;
             case 102:

src/main/java/edu/harvard/econcs/jopt/solver/mip/PoolSolution.java

@@ -41,10 +41,21 @@ public class PoolSolution implements Serializable, ISolution {
     private static final long serialVersionUID = -7265174347585717488L;
     private double objectiveValue;
     private final Map<String, Double> values;
+    private double relativeGap;
+    private double absoluteGap;
+    private double poolRelativeGap = -1;
+    private double poolAbsoluteGap = -1;
 
-    public PoolSolution(double objectiveValue, Map<String, Double> values) {
+    public PoolSolution(double objectiveValue, double bestObjectiveValue, Map<String, Double> values) {
         this.objectiveValue = objectiveValue;
         this.values = values;
+        this.absoluteGap = Math.abs(bestObjectiveValue - objectiveValue);
+        this.relativeGap = this.absoluteGap / (1e-10 + Math.abs(bestObjectiveValue));
+    }
+
+    public void setPoolGaps(double optimalObjectiveValue) {
+        this.poolAbsoluteGap = Math.abs(optimalObjectiveValue - objectiveValue);
+        this.poolRelativeGap = this.poolAbsoluteGap / (1e-10 + Math.abs(optimalObjectiveValue));
     }
 
     /*
@@ -135,10 +146,33 @@ public boolean equal(Object other) {
         return this.objectiveValue == otherSolution.getObjectiveValue() && getValues().equals(otherSolution.getValues());
     }
 
+    @Override
+    public double getRelativeGap() {
+        return relativeGap;
+    }
+
+    @Override
+    public double getAbsoluteGap() {
+        return absoluteGap;
+    }
+
 
     @Override
     public long getSolveTime() {
         return 0;
     }
 
+    /**
+     * @return The relative gap compared to the optimal feasible solution, if available. Else -1.
+     */
+    public double getPoolRelativeGap() {
+        return poolRelativeGap;
+    }
+
+    /**
+     * @return The absolute gap compared to the optimal feasible solution, if available. Else -1.
+     */
+    public double getPoolAbsoluteGap() {
+        return poolAbsoluteGap;
+    }
 }