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dlib
Commit 963bc0fe authored by Davis King's avatar Davis King
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merged

parents 973a3da3 9f53eac2
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Showing with 80 additions and 23 deletions
dlib/optimization/optimization_solve_qp_using_smo.h
View file @ 963bc0fe
......@@ -419,8 +419,8 @@ namespace dlib
matrix<T,NR,NC,MM,L>& alpha,
const matrix<T,NR,NC,MM,L>& lower,
const matrix<T,NR,NC,MM,L>& upper,
T eps,
unsigned long max_iter
T eps = 1e-10,
unsigned long max_iter = 30000
)
{
// make sure requires clause is not broken
......@@ -521,26 +521,35 @@ namespace dlib
v_old = v;
df = Q*alpha + b;
// now take a projected gradient step using Nesterov's method.
v = clamp(alpha - 1.0/lipschitz_bound * df, lower, upper);
alpha = dlib::clamp((1-gamma)*v + gamma*v_old, lower, upper);
df = Q*alpha + b;
// check for convergence every 10 iterations
if (iter%10 == 0)
{
max_df = 0;
double absalpha = 0;
double thealpha = 0;
for (long r = 0; r < Q.nr(); ++r)
{
absalpha += std::abs(alpha(r));
if (alpha(r) <= lower(r) && df(r) > 0)
;//alpha(r) = lower(r);
else if (alpha(r) >= upper(r) && df(r) < 0)
;//alpha(r) = upper(r);
else if (std::abs(df(r)) > max_df)
{
max_df = std::abs(df(r));
thealpha = std::abs(alpha(r));
}
}
if (max_df < eps)
absalpha /= Q.nr();
// Stop when the magnitude of the changes we are making to alpha are eps
// smaller than the typical alpha.
if (max_df/lipschitz_bound <= eps*std::min(thealpha,absalpha))
break;
}
}
......@@ -613,8 +622,8 @@ namespace dlib
std::vector<matrix<T,NR,NC,MM,L>>& alphas,
const std::vector<matrix<T,NR,NC,MM,L>>& lowers,
const std::vector<matrix<T,NR,NC,MM,L>>& uppers,
T eps,
unsigned long max_iter
T eps = 1e-10,
unsigned long max_iter = 30000
)
{
// make sure requires clause is not broken
......@@ -834,8 +843,6 @@ namespace dlib
v_old.swap(v);
//df = Q*alpha + b;
compute_df();
// now take a projected gradient step using Nesterov's method.
for (size_t j = 0; j < alphas.size(); ++j)
......@@ -844,11 +851,15 @@ namespace dlib
alphas[j] = clamp((1-gamma)*v[j] + gamma*v_old[j], lowers[j], uppers[j]);
}
//df = Q*alpha + b;
compute_df();
// check for convergence every 10 iterations
if (iter%10 == 0)
{
max_df = 0;
double absalpha = 0;
double thealpha = 0;
for (size_t r2 = 0; r2 < alphas.size(); ++r2)
{
auto& alpha = alphas[r2];
......@@ -857,15 +868,22 @@ namespace dlib
auto& upper = uppers[r2];
for (long r = 0; r < alpha.nr(); ++r)
{
absalpha += std::abs(alpha(r));
if (alpha(r) <= lower(r) && df_(r) > 0)
;//alpha(r) = lower(r);
else if (alpha(r) >= upper(r) && df_(r) < 0)
;//alpha(r) = upper(r);
else if (std::abs(df_(r)) > max_df)
{
max_df = std::abs(df_(r));
thealpha = std::abs(alpha(r));
}
}
}
if (max_df < eps)
absalpha /= num_variables;
// Stop when the magnitude of the changes we are making to alpha are eps
// smaller than the typical alpha.
if (max_df/lipschitz_bound <= eps*std::min(thealpha,absalpha))
break;
}
}
......
dlib/optimization/optimization_solve_qp_using_smo_abstract.h
View file @ 963bc0fe
......@@ -128,8 +128,8 @@ namespace dlib
matrix<T,NR,NC,MM,L>& alpha,
const matrix<T,NR,NC,MM,L>& lower,
const matrix<T,NR,NC,MM,L>& upper,
T eps,
unsigned long max_iter
T eps = 1e-10,
unsigned long max_iter = 30000
);
/*!
requires
......@@ -155,10 +155,10 @@ namespace dlib
- The solution to the above QP will be stored in #alpha.
- This function uses a combination of a SMO algorithm along with Nesterov's
method as the main iteration of the solver. It starts the algorithm with the
given alpha and it works on the problem until the derivative of f(alpha) is
smaller than eps for each element of alpha or the alpha value is at a box
constraint. So eps controls how accurate the solution is and smaller values
result in better solutions.
given alpha and works on the problem until the magnitude of the changes we
are making to alpha are eps times smaller than the typical values in alpha.
So eps controls how accurate the solution is and smaller values result in
better solutions.
- At most max_iter iterations of optimization will be performed.
- returns the number of iterations performed. If this method fails to
converge to eps accuracy then the number returned will be max_iter+1.
......@@ -176,8 +176,8 @@ namespace dlib
std::vector<matrix<T,NR,NC,MM,L>>& alphas,
const std::vector<matrix<T,NR,NC,MM,L>>& lowers,
const std::vector<matrix<T,NR,NC,MM,L>>& uppers,
T eps,
unsigned long max_iter
T eps = 1e-10,
unsigned long max_iter = 30000
);
/*!
requires
......@@ -224,10 +224,10 @@ namespace dlib
- The solution to the above QP will be stored in #alphas.
- This function uses a combination of a SMO algorithm along with Nesterov's
method as the main iteration of the solver. It starts the algorithm with the
given alpha and it works on the problem until the derivative of f(alpha) is
smaller than eps for each element of alpha or the alpha value is at a box
constraint. So eps controls how accurate the solution is and smaller values
result in better solutions.
given alpha and works on the problem until the magnitude of the changes we
are making to alpha are eps times smaller than the typical values in alpha.
So eps controls how accurate the solution is and smaller values result in
better solutions.
- At most max_iter iterations of optimization will be performed.
- returns the number of iterations performed. If this method fails to
converge to eps accuracy then the number returned will be max_iter+1.
......
dlib/test/mpc.cpp
View file @ 963bc0fe
......@@ -328,8 +328,8 @@ namespace
lower = -4;
upper = 3;
solve_qp_box_using_smo(Q,b,alpha,lower, upper, 0.000000001, 500000);
solve_qp_box_constrained(Q,b,alpha2,lower, upper, 0.000000001, 50000);
solve_qp_box_using_smo(Q,b,alpha,lower, upper, 1e-12, 500000);
solve_qp_box_constrained(Q,b,alpha2,lower, upper, 1e-12, 50000);
dlog << LINFO << trans(alpha);
dlog << LINFO << trans(alpha2);
dlog << LINFO << "objective value: " << 0.5*trans(alpha)*Q*alpha + trans(b)*alpha;
......
dlib/test/opt_qp_solver.cpp
View file @ 963bc0fe
......@@ -590,6 +590,44 @@ namespace
}
}
// ----------------------------------------------------------------------------------------
void test_solve_qp_box_constrained()
{
dlog << LINFO << "test_solve_qp_box_constrained()";
print_spinner();
dlib::rand rnd;
matrix<double> m = randm(6,6,rnd);
m = m*trans(m);
matrix<double,0,1> b = randm(6,1,rnd)-0.5;
matrix<double,0,1> lower, upper, solution;
lower = -ones_matrix<double>(6,1)*1e100;
upper = ones_matrix<double>(6,1)*1e100;
solution = zeros_matrix(lower);
unsigned long iters = solve_qp_box_constrained(m,b,solution, lower, upper);
dlog << LINFO << "iters: " << iters;
matrix<double> true_solution = -pinv(m)*b;
DLIB_TEST_MSG(max(abs(solution - true_solution)) < 1e-6, max(abs(solution - true_solution)));
iters = solve_qp_box_constrained(m,b,solution, lower, upper, 1e-14);
dlog << LINFO << "iters: " << iters;
/*
const double obj1 = 0.5*trans(solution)*m*solution + trans(solution)*b;
const double obj2 = 0.5*trans(true_solution)*m*true_solution + trans(true_solution)*b;
cout << "iters:" << iters << endl;
cout << "obj1: "<< obj1 << endl;
cout << "obj2: "<< obj2 << endl;
cout << "obj1-obj2: "<< obj1-obj2 << endl;
*/
DLIB_TEST_MSG(max(abs(solution - true_solution)) < 1e-10, max(abs(solution - true_solution)));
}
// ----------------------------------------------------------------------------------------
void test_solve_qp_box_constrained_blockdiag_compact(dlib::rand& rnd, double percent_off_diag_present)
......@@ -706,6 +744,7 @@ namespace
void perform_test(
)
{
test_solve_qp_box_constrained();
print_spinner();
test_solve_qp4_using_smo();
print_spinner();
......
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