Optimum location of point to minimize total distance - GeeksforGeeks
Given a set of points as and a line as ax+by+c = 0. We need to find a point on given line for which sum of distances from given set of points is minimum.
https://en.wikipedia.org/wiki/Ternary_search
Read full article from Optimum location of point to minimize total distance - GeeksforGeeks
Given a set of points as and a line as ax+by+c = 0. We need to find a point on given line for which sum of distances from given set of points is minimum.
If we take one point on given line at infinite distance then total distance cost will be infinite, now when we move this point on line towards given points the total distance cost starts decreasing and after some time, it again starts increasing which reached to infinite on the other infinite end of line so distance cost curve looks like a U-curve and we have to find the bottom value of this U-curve.
As U-curve is not monotonically increasing or decreasing we can’t use binary search for finding bottom most point, here we will use ternary search for finding bottom most point, ternary search skips one third of search space at each iteration, you can read more about ternary search here.
So solution proceeds as follows, we start with low and high initialized as some smallest and largest values respectively, then we start iteration, in each iteration we calculate two mids, mid1 and mid2, which represent 1/3rd and 2/3rd position in search space, we calculate total distance of all points with mid1 and mid2 and update low or high by comparing these distance cost, this iteration continues untill low and high become approximately equal.
As U-curve is not monotonically increasing or decreasing we can’t use binary search for finding bottom most point, here we will use ternary search for finding bottom most point, ternary search skips one third of search space at each iteration, you can read more about ternary search here.
So solution proceeds as follows, we start with low and high initialized as some smallest and largest values respectively, then we start iteration, in each iteration we calculate two mids, mid1 and mid2, which represent 1/3rd and 2/3rd position in search space, we calculate total distance of all points with mid1 and mid2 and update low or high by comparing these distance cost, this iteration continues untill low and high become approximately equal.
// structure defining a point
struct
point
{
int
x, y;
point() {}
point(
int
x,
int
y) : x(x), y(y) {}
};
// structure defining a line of ax + by + c = 0 form
struct
line
{
int
a, b, c;
line(
int
a,
int
b,
int
c) : a(a), b(b), c(c) {}
};
// method to get distance of point (x, y) from point p
double
dist(
double
x,
double
y, point p)
{
return
sqrt
(sq(x - p.x) + sq(y - p.y));
}
/* Utility method to compute total distance all points
when choose point on given line has x-cordinate
value as X */
double
compute(point p[],
int
n, line l,
double
X)
{
double
res = 0;
// calculating Y of choosen point by line equation
double
Y = -1 * (l.c + l.a*X) / l.b;
for
(
int
i = 0; i < n; i++)
res += dist(X, Y, p[i]);
return
res;
}
// Utility method to find minimum total distance
double
findOptimumCostUtil(point p[],
int
n, line l)
{
double
low = -1e6;
double
high = 1e6;
// loop untill difference between low and high
// become less than EPS
while
((high - low) > EPS)
{
// mid1 and mid2 are representative x co-ordiantes
// of search space
double
mid1 = low + (high - low) / 3;
double
mid2 = high - (high - low) / 3;
//
double
dist1 = compute(p, n, l, mid1);
double
dist2 = compute(p, n, l, mid2);
// if mid2 point gives more total distance,
// skip third part
if
(dist1 < dist2)
high = mid2;
// if mid1 point gives more total distance,
// skip first part
else
low = mid1;
}
// compute optimum distance cost by sending average
// of low and high as X
return
compute(p, n, l, (low + high) / 2);
}
// method to find optimum cost
double
findOptimumCost(
int
points[N][2], line l)
{
point p[N];
// converting 2D array input to point array
for
(
int
i = 0; i < N; i++)
p[i] = point(points[i][0], points[i][1]);
return
findOptimumCostUtil(p, N, l);
}
https://en.wikipedia.org/wiki/Ternary_search
def ternarySearch(f, left, right, absolutePrecision):
'''
left and right are the current bounds;
the maximum is between them
'''
if abs(right - left) < absolutePrecision:
return (left + right)/2
leftThird = (2*left + right)/3
rightThird = (left + 2*right)/3
if f(leftThird) < f(rightThird):
return ternarySearch(f, leftThird, right, absolutePrecision)
else:
return ternarySearch(f, left, rightThird, absolutePrecision)
def ternarySearch(f, left, right, absolutePrecision):
"""
Find maximum of unimodal function f() within [left, right]
To find the minimum, revert the if/else statement or revert the comparison.
"""
while True:
#left and right are the current bounds; the maximum is between them
if abs(right - left) < absolutePrecision:
return (left + right)/2
leftThird = left + (right - left)/3
rightThird = right - (right - left)/3
if f(leftThird) < f(rightThird):
left = leftThird
else:
right = rightThird