diff --git a/src/transmissionReconstruction/reconstruction/solvingEquationSystem/TVAL/TVAL.cpp b/src/transmissionReconstruction/reconstruction/solvingEquationSystem/TVAL/TVAL.cpp
index 715ace1..9867575 100644
--- a/src/transmissionReconstruction/reconstruction/solvingEquationSystem/TVAL/TVAL.cpp
+++ b/src/transmissionReconstruction/reconstruction/solvingEquationSystem/TVAL/TVAL.cpp
@@ -54,141 +54,13 @@ namespace Recon
     }
 
     void checkAndScale(Aurora::Sparse& M, Aurora::Matrix& b,size_t n){
-        return;
-        //TODO:暂时啥都没做
-        
-        //无用的代码？
-        //opts.scale_A = true;
-        //opts.consist_mu = false;
-        sparse_matrix_t A;
-        size_t length = M.getRowVector().getDataSize();
-        int *row_indx = new int[length];
-        int *col_indx = new int[length];
-        size_t rows = M.getM(), cols = M.getN();
-        std::copy(M.getRowVector().getData(),M.getRowVector().getData()+length,row_indx);
-        std::copy(M.getColVector().getData(),M.getColVector().getData()+length,col_indx);
-        auto status = mkl_sparse_d_create_coo(&A,sparse_index_base_t::SPARSE_INDEX_BASE_ZERO,rows,cols, M.getValVector().getDataSize(),
-             row_indx, col_indx, M.getValVector().getData());
-        if(status != SPARSE_STATUS_SUCCESS)
-        {
-            std::cerr<<"create mkl sparse error!checkAndScale fail!"<<std::endl;
-            return;
-        }
-        int *row_indx2 = new int[n];
-        int *col_indx2 = new int[n];
-        for (size_t i = 0; i < n; i++)
-        {
-            row_indx2[i] = i;
-            col_indx2[i] = i;
-        }
-        double * value2 = new double[n]{1};
-        sparse_matrix_t eye;
-        status = mkl_sparse_d_create_coo(&eye,sparse_index_base_t::SPARSE_INDEX_BASE_ZERO,n, n, n,
-             row_indx2, col_indx2, value2);
-        if(status != SPARSE_STATUS_SUCCESS)
-        {
-            std::cerr<<"create eye mkl sparse error!checkAndScale fail!"<<std::endl;
-            return;
-        }
-        std::vector<int> vCols;
-        {
-            int *col_indx2 = new int[length];
-            std::copy(M.getColVector().getData(),M.getColVector().getData()+length,col_indx2);
-            std::sort(col_indx2,col_indx2+length);
-            vCols.push_back(col_indx2[0]);
-            double lastV = col_indx2[0];
-            for (size_t i = 1; i < length; i++)
-            {
-                if(col_indx2[i]==lastV)continue;
-                vCols.push_back(col_indx2[i]);
-                lastV = col_indx2[i];
-            }
-                    
-        }
-        double * vec = new double[M.getN()]{0};
-        double * vecOut1 = new double[M.getM()]{0};
-        double * vecOut2 = new double[M.getN()]{0};
-        int colIdxIdx = 0;
-        for (size_t i = 0; i < 6000; i++)
-        {
-            if ( vCols[colIdxIdx]>i)continue;
-            else colIdxIdx++;
-            vec[i]=1;
-            if (i-1>0)vec[i-1] = 0;
-            matrix_descr descr;
-            descr.type = SPARSE_MATRIX_TYPE_GENERAL;
-            status = mkl_sparse_d_mv(sparse_operation_t::SPARSE_OPERATION_NON_TRANSPOSE, 1.0, A, descr, vec, 0.0, vecOut1);
-            if(status != SPARSE_STATUS_SUCCESS)
-            {
-                std::cerr<<"sparse * vector error!checkAndScale fail!"<<std::endl;
-                return;
-            }
-            status = mkl_sparse_d_mv(sparse_operation_t::SPARSE_OPERATION_TRANSPOSE, 1.0, A, descr, vecOut1, 0.0, vecOut2);
-            if(status != SPARSE_STATUS_SUCCESS)
-            {
-                std::cerr<<"sparse * vector error!checkAndScale fail!"<<std::endl;
-                return;
-            }
-            for (size_t j = 0; j < M.getN(); j++)
-            {
-                if (vecOut2[j]!=0.0){
-                    mkl_sparse_d_set_value(eye, j, i, vecOut2[j]);
-                }   
-            }
-        }
-        delete [] row_indx;
-        delete [] col_indx;
-        delete [] vec;
-        delete [] vecOut1;
-        delete [] vecOut2;
-        matrix_descr descr;
-        descr.type = SPARSE_MATRIX_TYPE_GENERAL;
-        int k = 0;
-        int pm[128]{0};
-        mkl_sparse_ee_init(pm);
-        pm[1]=3;
-        double s2 = 0.0, res = 0.0;
-        double *X = new double[n];
-        char which = 'L';
-        status = mkl_sparse_d_ev(&which, pm, eye, descr, 1, &k, &s2, X, &res);
-        if(status != SPARSE_STATUS_SUCCESS)
-        {
-            std::cerr<<"sparse eigen error!checkAndScale fail!"<<std::endl;
-            return;
-        }
-        status = mkl_sparse_destroy(eye);
-        if(status != SPARSE_STATUS_SUCCESS)
-        {
-            std::cerr<<"sparse destory error!checkAndScale fail!"<<std::endl;
-            return;
-        }
-        delete [] row_indx2;
-        delete [] col_indx2;
-        delete [] value2;
-        delete [] X;
-        if (s2> 1 + 1e-10){
-            b = b/sqrt(s2);
-            M.getValVector() = M.getValVector()/sqrt(s2);
-        }
-        //TODO:以下意义不明,暂定为判断是否为复数存储
-        //if ~isreal(M * rand(n,1))
-        //  eopts.isreal = false;
-        //end
-        status = mkl_sparse_destroy(A);
-        if(status != SPARSE_STATUS_SUCCESS)
-        {
-            std::cerr<<"sparse destory error!checkAndScale fail!"<<std::endl;
-            return;
-        }
         bool isreal = M.getValVector().getValueType() == Aurora::Normal;
-
-        //TODO：以下操作意义不明
-        //fh = @(x) ((M * x)' * M)';   
-        //s2 = eigs(fh,n,1,'lm',eopts);
-        //if real(s2) > 1 + 1e-10
-        //    b = b ./ sqrt(s2);
-        //    M = M ./ sqrt( s2);
-        //end
+        auto s2 = eigs(M);
+        if (s2> 1 + 1e-10)
+        {
+            b = b / sqrt(s2);
+            M.getValVector() = M.getValVector() / sqrt( s2);
+        }
     }
 
     Aurora::Matrix callTval3(Aurora::Sparse& M, Aurora::Matrix& b,const Aurora::Matrix& dims,int device, TVALOptions& opt)