LinearSolver.Builder

optimatika · Jul 30, 2024 · 23ffcce · 23ffcce
1 parent 5af4ef4
commit 23ffcce
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -11,6 +11,13 @@ Added / Changed / Deprecated / Fixed / Removed / Security
 
 > Corresponds to changes in the `develop` branch since the last release
 
+### Changed
+
+#### org.ojalgo.optimisation
+
+- The new LP solver implementation is now the default alternative.
+- The two classes `LinearSolver.GeneralBuilder` and `LinearSolver.StandardBuilder` are replaced by a common `LinearSolver.Builder`. Consequently the methods `newGeneralBuilder()` and `newStandardBuilder()` are deprecated and replaced by `newBuilder()`.
+
 ## [54.0.0] – 2024-06-06
 
 ### Added

diff --git a/src/main/java/org/ojalgo/optimisation/convex/ConvexSolver.java b/src/main/java/org/ojalgo/optimisation/convex/ConvexSolver.java
@@ -201,15 +201,15 @@ public Builder quadratic(final Access2D<?> factors) {
         /**
          * Disregard the objective function (set it to zero) and form the dual LP.
          */
-        public LinearSolver.GeneralBuilder toFeasibilityChecker() {
+        public LinearSolver.Builder toFeasibilityChecker() {
 
             MatrixStore<Double> mtrxAE = this.getAE();
             MatrixStore<Double> mtrxBE = this.getBE();
 
             MatrixStore<Double> mtrxAI = this.getAI();
             MatrixStore<Double> mtrxBI = this.getBI();
 
-            LinearSolver.GeneralBuilder retVal = LinearSolver.newGeneralBuilder();
+            LinearSolver.Builder retVal = LinearSolver.newBuilder();
 
             int nbEqus = this.countEqualityConstraints();
             int nbIneq = this.countInequalityConstraints();
@@ -253,15 +253,15 @@ public LinearSolver.GeneralBuilder toFeasibilityChecker() {
          *
          * @see #toLinearApproximation(Access1D)
          */
-        public LinearSolver.GeneralBuilder toLinearApproximation() {
+        public LinearSolver.Builder toLinearApproximation() {
             return this.toLinearApproximation(ArrayR064.make(this.countVariables()));
         }
 
         /**
          * Linearise the objective function (at the specified point) and duplicate all variables to handle the
          * (potential) positive and negative parts separately.
          */
-        public LinearSolver.GeneralBuilder toLinearApproximation(final Access1D<Double> point) {
+        public LinearSolver.Builder toLinearApproximation(final Access1D<Double> point) {
 
             MatrixStore<Double> mtrxC = this.getObjective().toFirstOrderApproximation(point).getLinearFactors(false);
 
@@ -271,7 +271,7 @@ public LinearSolver.GeneralBuilder toLinearApproximation(final Access1D<Double>
             MatrixStore<Double> mtrxAI = this.getAI();
             MatrixStore<Double> mtrxBI = this.getBI();
 
-            LinearSolver.GeneralBuilder retVal = LinearSolver.newGeneralBuilder();
+            LinearSolver.Builder retVal = LinearSolver.newBuilder();
 
             retVal.objective(mtrxC.below(mtrxC.negate()));
 

diff --git a/src/main/java/org/ojalgo/optimisation/integer/IntegerSolver.java b/src/main/java/org/ojalgo/optimisation/integer/IntegerSolver.java
@@ -204,10 +204,12 @@ public Result solve(final Result kickStarter) {
 
         this.resetIterationsCount();
 
-        ExpressionsBasedModel cutModel = myIntegerModel.snapshot();
-        NodeSolver cutSolver = cutModel.prepare(NodeSolver::new);
-        Result cutResult = cutSolver.solve();
-        cutSolver.generateCuts(strategy, myIntegerModel);
+        if (strategy.cutting) {
+            ExpressionsBasedModel cutModel = myIntegerModel.snapshot();
+            NodeSolver cutSolver = cutModel.prepare(NodeSolver::new);
+            Result cutResult = cutSolver.solve();
+            cutSolver.generateCuts(strategy, myIntegerModel);
+        }
 
         NodeKey rootNode = new NodeKey(myIntegerModel);
         ExpressionsBasedModel rootModel = myIntegerModel.snapshot();
@@ -520,7 +522,7 @@ boolean compute(final NodeKey nodeKey, final NodeSolver nodeSolver, final RingLo
             IntegerSolver.flush(nodePrinter, myIntegerModel.options.logger_appender);
         }
 
-        if (strategy.cutting && nodeKey.sequence % 10L == 0L) {
+        if (strategy.cutting && nodeKey.sequence % 100L == 0L) {
             double displacement = nodeKey.getMinimumDisplacement(branchIntegerIndex, variableValue);
             if (strategy.isCutRatherThanBranch(displacement, myBestResultSoFar != null)) {
                 if (nodeSolver.generateCuts(strategy, nodeKey)) {