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钟尚武
dlib
Commits
c290381b
Commit
c290381b
authored
Jun 02, 2015
by
Davis King
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Plain Diff
Made the solver use an SMO type iteration in the beginning before
switching to projected gradient steps.
parent
1a494a91
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Showing
1 changed file
with
40 additions
and
9 deletions
+40
-9
mpc.h
dlib/control/mpc.h
+40
-9
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dlib/control/mpc.h
View file @
c290381b
...
...
@@ -118,6 +118,7 @@ namespace dlib
for
(
unsigned
long
c
=
0
;
c
<
horizon
;
++
c
)
{
lambda
+=
trace
(
trans
(
B
)
*
temp
*
B
);
Q_diag
[
horizon
-
c
-
1
]
=
diag
(
trans
(
B
)
*
temp
*
B
);
temp
=
trans
(
A
)
*
temp
*
A
+
diagm
(
Q
);
}
...
...
@@ -264,9 +265,6 @@ namespace dlib
for
(
unsigned
long
i
=
0
;
i
<
horizon
;
++
i
)
MM
[
i
]
=
trans
(
B
)
*
M
[
i
];
for
(
unsigned
long
i
=
0
;
i
<
horizon
;
++
i
)
v
[
i
]
=
controls
[
i
];
unsigned
long
iter
=
0
;
...
...
@@ -289,6 +287,8 @@ namespace dlib
// Check the stopping condition, which is the magnitude of the largest element
// of the gradient.
double
max_df
=
0
;
unsigned
long
max_t
=
0
;
long
max_v
=
0
;
for
(
unsigned
long
i
=
0
;
i
<
horizon
;
++
i
)
{
for
(
long
j
=
0
;
j
<
controls
[
i
].
size
();
++
j
)
...
...
@@ -298,7 +298,12 @@ namespace dlib
if
(
!
((
controls
[
i
](
j
)
<=
lower
(
j
)
&&
df
[
i
](
j
)
>
0
)
||
(
controls
[
i
](
j
)
>=
upper
(
j
)
&&
df
[
i
](
j
)
<
0
)))
{
max_df
=
std
::
max
(
max_df
,
std
::
abs
(
df
[
i
](
j
)));
if
(
std
::
abs
(
df
[
i
](
j
))
>
max_df
)
{
max_df
=
std
::
abs
(
df
[
i
](
j
));
max_t
=
i
;
max_v
=
j
;
}
}
}
}
...
...
@@ -306,12 +311,37 @@ namespace dlib
break
;
// take a step based on the gradient
for
(
unsigned
long
i
=
0
;
i
<
horizon
;
++
i
)
// We will start out by doing a little bit of coordinate descent because it
// allows us to optimize individual variables exactly. Since we are warm
// starting each iteration with a really good solution this helps speed
// things up a lot.
const
unsigned
long
smo_iters
=
50
;
if
(
iter
<
smo_iters
)
{
if
(
Q_diag
[
max_t
](
max_v
)
==
0
)
continue
;
// Take the optimal step but just for one variable.
controls
[
max_t
](
max_v
)
=
-
(
df
[
max_t
](
max_v
)
-
Q_diag
[
max_t
](
max_v
)
*
controls
[
max_t
](
max_v
))
/
Q_diag
[
max_t
](
max_v
);
controls
[
max_t
](
max_v
)
=
put_in_range
(
lower
(
max_v
),
upper
(
max_v
),
controls
[
max_t
](
max_v
));
// If this is the last SMO iteration then don't forget to initialize v
// for the gradient steps.
if
(
iter
+
1
==
smo_iters
)
{
for
(
unsigned
long
i
=
0
;
i
<
horizon
;
++
i
)
v
[
i
]
=
controls
[
i
];
}
}
else
{
v_old
[
i
]
=
v
[
i
];
v
[
i
]
=
clamp
(
controls
[
i
]
-
1
.
0
/
lambda
*
df
[
i
],
lower
,
upper
);
controls
[
i
]
=
clamp
(
v
[
i
]
+
(
std
::
sqrt
(
lambda
)
-
1
)
/
(
std
::
sqrt
(
lambda
)
+
1
)
*
(
v
[
i
]
-
v_old
[
i
]),
lower
,
upper
);
// Take a projected gradient step.
for
(
unsigned
long
i
=
0
;
i
<
horizon
;
++
i
)
{
v_old
[
i
]
=
v
[
i
];
v
[
i
]
=
clamp
(
controls
[
i
]
-
1
.
0
/
lambda
*
df
[
i
],
lower
,
upper
);
controls
[
i
]
=
clamp
(
v
[
i
]
+
(
std
::
sqrt
(
lambda
)
-
1
)
/
(
std
::
sqrt
(
lambda
)
+
1
)
*
(
v
[
i
]
-
v_old
[
i
]),
lower
,
upper
);
}
}
}
}
...
...
@@ -329,6 +359,7 @@ namespace dlib
matrix
<
double
,
S
,
1
>
target
[
horizon
];
double
lambda
;
// abound on the largest eigenvalue of the hessian matrix.
matrix
<
double
,
I
,
1
>
Q_diag
[
horizon
];
matrix
<
double
,
I
,
1
>
controls
[
horizon
];
};
...
...
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