507       Matrix &edgeCosts = g.getEdgeCosts(eItr);  in tryNormaliseEdgeMatrix()  local
511       for (unsigned r = 0; r < edgeCosts.getRows(); ++r) {  in tryNormaliseEdgeMatrix()
514         for (unsigned c = 0; c < edgeCosts.getCols(); ++c) {  in tryNormaliseEdgeMatrix()
515           if (vCosts[c] != infinity && edgeCosts[r][c] < rowMin)  in tryNormaliseEdgeMatrix()
516             rowMin = edgeCosts[r][c];  in tryNormaliseEdgeMatrix()
522           edgeCosts.subFromRow(r, rowMin);  in tryNormaliseEdgeMatrix()
525           edgeCosts.setRow(r, 0);  in tryNormaliseEdgeMatrix()
529       for (unsigned c = 0; c < edgeCosts.getCols(); ++c) {  in tryNormaliseEdgeMatrix()
532         for (unsigned r = 0; r < edgeCosts.getRows(); ++r) {  in tryNormaliseEdgeMatrix()
533           if (uCosts[r] != infinity && edgeCosts[r][c] < colMin)  in tryNormaliseEdgeMatrix()
534             colMin = edgeCosts[r][c];  in tryNormaliseEdgeMatrix()
540           edgeCosts.subFromCol(c, colMin);  in tryNormaliseEdgeMatrix()
543           edgeCosts.setCol(c, 0);  in tryNormaliseEdgeMatrix()
547       return edgeCosts.isZero();  in tryNormaliseEdgeMatrix()
569         Matrix &edgeCosts = g.getEdgeCosts(eItr);  in computeSolution()  local
574           v += edgeCosts.getColAsVector(adjSolution);  in computeSolution()
579           v += edgeCosts.getRowAsVector(adjSolution);  in computeSolution()