GCC Code Coverage Report

Directory: ./
File: tasks/kruglova_a_conjugate_gradient_sle/seq/src/ops_seq.cpp
Date: 2026-04-02 17:12:27
Exec Total Coverage
Lines: 40 41 97.6%
Functions: 6 6 100.0%
Branches: 23 32 71.9%
Line Branch Exec Source
1 #include "kruglova_a_conjugate_gradient_sle/seq/include/ops_seq.hpp"
2
3 #include <cmath>
4 #include <cstddef>
5 #include <vector>
6
7 #include "kruglova_a_conjugate_gradient_sle/common/include/common.hpp"
8
9 namespace kruglova_a_conjugate_gradient_sle {
10
11 namespace {
12 264 void MatrixVectorMultiply(const std::vector<double> &a, const std::vector<double> &p, std::vector<double> &ap, int n) {
13
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 11280 for (int i = 0; i < n; ++i) {
14 11016 ap[i] = 0.0;
15
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 852160 for (int j = 0; j < n; ++j) {
16 841144 ap[i] += a[(i * n) + j] * p[j];
17 }
18 }
19 264 }
20 } // namespace
21
22
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 48 KruglovaAConjGradSleSEQ::KruglovaAConjGradSleSEQ(const InType &in) {
23 SetTypeOfTask(GetStaticTypeOfTask());
24 GetInput() = in;
25 48 }
26
27 48 bool KruglovaAConjGradSleSEQ::ValidationImpl() {
28 const auto &in = GetInput();
29
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 48 if (in.size <= 0) {
30 return false;
31 }
32
33
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 48 if (in.A.size() != static_cast<size_t>(in.size) * static_cast<size_t>(in.size)) {
34 return false;
35 }
36
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 48 if (in.b.size() != static_cast<size_t>(in.size)) {
37 ✗ return false;
38 }
39 return true;
40 }
41
42 48 bool KruglovaAConjGradSleSEQ::PreProcessingImpl() {
43 48 GetOutput().assign(GetInput().size, 0.0);
44 48 return true;
45 }
46
47 48 bool KruglovaAConjGradSleSEQ::RunImpl() {
48 48 const auto &a = GetInput().A;
49 48 const auto &b = GetInput().b;
50 48 int n = GetInput().size;
51 auto &x = GetOutput();
52
53 48 std::vector<double> r = b;
54
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 48 std::vector<double> p = r;
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 48 std::vector<double> ap(n, 0.0);
56
57 double rsold = 0.0;
58
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 1408 for (int i = 0; i < n; ++i) {
59 1360 rsold += r[i] * r[i];
60 }
61
62 const double tolerance = 1e-8;
63
64
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 264 for (int iter = 0; iter < n * 2; ++iter) {
65 264 MatrixVectorMultiply(a, p, ap, n);
66
67 double p_ap = 0.0;
68
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 11280 for (int i = 0; i < n; ++i) {
69 11016 p_ap += p[i] * ap[i];
70 }
71
72 if (std::abs(p_ap) < 1e-15) {
73 break;
74 }
75
76 264 double alpha = rsold / p_ap;
77
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 11280 for (int i = 0; i < n; ++i) {
78 11016 x[i] += alpha * p[i];
79 11016 r[i] -= alpha * ap[i];
80 }
81
82 double rsnew = 0.0;
83
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 11280 for (int i = 0; i < n; ++i) {
84 11016 rsnew += r[i] * r[i];
85 }
86
87
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 264 if (std::sqrt(rsnew) < tolerance) {
88 break;
89 }
90
91
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 9872 for (int i = 0; i < n; ++i) {
92 9656 p[i] = r[i] + ((rsnew / rsold) * p[i]);
93 }
94 rsold = rsnew;
95 }
96 48 return true;
97 }
98
99 48 bool KruglovaAConjGradSleSEQ::PostProcessingImpl() {
100 48 return true;
101 }
102
103 } // namespace kruglova_a_conjugate_gradient_sle
104