initial upload

archive/xyz2shc/optimize_cg.cpp

@@ -0,0 +1,105 @@
+#include "xyz2shc.h"
+
+void XYZ2SHC::Optimize_CG(){
+	int i,j;
+	double data_object_func, PartB_m,gradient_m,gradient_m1,dTAd,ak,beta_k1;
+	_1dArray gradientk, dk, Adk, Adk_part, predict_field;
+	//reserve vector
+	gradientk.resize(shc_num_); dk.resize(shc_num_);
+	Adk.resize(shc_num_); Adk_part.resize(obs_num_);
+	predict_field.resize(obs_num_);
+
+#pragma omp parallel for private(i,j) schedule(guided)
+	for (i = 0; i < obs_num_; i++){
+		Adk_part[i] = 0.0;
+		for (j = 0; j < shc_num_; j++){
+			Adk_part[i] += c_kernel_[i][j]*shc_value_[j];
+		}
+	}
+
+#pragma omp parallel for private(i,j) schedule(guided)
+	for (j = 0; j < shc_num_; j++){
+		Adk[j] = 0.0;
+		for (i = 0; i < obs_num_; i++){
+			Adk[i] += c_kernel_[i][j]*wdTwd_[i]*Adk_part[i];
+		}
+	}
+
+#pragma omp parallel for private(i) schedule(guided)
+	for (j = 0; j < shc_num_; j++){
+		gradientk[j] = Adk[j] - PartB_[j];
+		dk[j] = -1.0*gradientk[j];
+	}
+
+	PartB_m = 0.0;
+	gradient_m = 0.0;
+	for (j = 0; j < shc_num_; j++){
+		PartB_m += PartB_[j]*PartB_[j];
+		gradient_m += gradientk[i]*gradientk[i];
+	}
+
+	for (int time = 0; time < iter_times_; time++){
+		//计算数据拟合差函数
+#pragma omp parallel for private(i,j) schedule(guided)
+		for (i = 0; i < obs_num_; i++){
+			predict_field[i] = 0.0;
+			for (j = 0; j < shc_num_; j++){
+				predict_field[i] += c_kernel_[i][j] * shc_value_[j];
+			}
+		}
+
+		data_object_func = 0.0;
+		for (i = 0; i < obs_num_; i++){
+			data_object_func += pow((obs_point_[i].val - predict_field[i])/obs_point_[i].dev,2);
+		}
+		data_object_func /= obs_num_;
+
+		if (data_object_func <= 1.0){
+			cout << "Iteration limit rearched!\tconvergence value = " << data_object_func << endl;
+			cout << ">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>" << endl;
+			break;
+		}
+		else{
+			cout << "Iteration times: " << time << "\tconvergence value = " << data_object_func << endl;
+			MOVEUP(1);
+			cout << CLEARLINE;
+		}
+
+#pragma omp parallel for private(i,j) schedule(guided)
+		for (i = 0; i < obs_num_; i++){
+			Adk_part[i] = 0.0;
+			for (j = 0; j < shc_num_; j++){
+				Adk_part[i] += c_kernel_[i][j]*dk[j];
+			}
+		}
+
+#pragma omp parallel for private(i,j) schedule(guided)
+		for (j = 0; j < shc_num_; j++){
+			Adk[j] = 0.0;
+			for (i = 0; i < obs_num_; i++){
+				Adk[i] += c_kernel_[i][j]*wdTwd_[i]*Adk_part[i];
+			}
+		}
+
+		dTAd = 0.0;
+		for (j = 0; j < shc_num_; j++){
+			dTAd += dk[j]*Adk[j];
+		}
+
+		ak = gradient_m/dTAd;
+
+		gradient_m1 = 0;
+		for (j = 0; j < shc_num_; j++){
+			shc_value_[j] += ak*dk[j];
+			gradientk[j] += ak*Adk[j];
+			gradient_m1 += gradientk[j]*gradientk[j];
+		}
+
+		beta_k1 = gradient_m1/gradient_m;
+		gradient_m = gradient_m1;
+		for (j = 0; j < shc_num_; j++){
+			dk[j] = beta_k1*dk[j] - gradientk[j];
+		}
+	}
+	return;
+}