Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
package breeze.optimize
import breeze.util.SerializableLogging
abstract class CubicLineSearch extends SerializableLogging with MinimizingLineSearch {
import scala.math._
case class Bracket(
t: Double, // 1d line search parameter
dd: Double, // Directional Derivative at t
fval: Double // Function value at t
)
/*
* Invoke line search, returning stepsize
*/
def minimize(f: DiffFunction[Double], init: Double = 1.0): Double
/*
* Cubic interpolation to find the minimum inside the bracket l and r.
* Uses the fval and gradient at the left and right side, which gives
* the four bits of information required to interpolate a cubic.
* This is additionally "safe-guarded" whereby steps too close to
* either side of the interval will not be selected.
*/
def interp(l: Bracket, r: Bracket) = {
// See N&W p57 actual for an explanation of the math
val d1 = l.dd + r.dd - 3 * (l.fval - r.fval) / (l.t - r.t)
val d2 = sqrt(d1 * d1 - l.dd * r.dd)
val multipler = r.t - l.t
val t = r.t - multipler * (r.dd + d2 - d1) / (r.dd - l.dd + 2 * d2)
// If t is too close to either end bracket, move it closer to the middle
val lbound = l.t + 0.1 * (r.t - l.t)
val ubound = l.t + 0.9 * (r.t - l.t)
t match {
case _ if t < lbound =>
logger.debug("Cubic " + t + " below LHS limit: " + lbound)
lbound
case _ if t > ubound =>
logger.debug("Cubic " + t + " above RHS limit: " + ubound)
ubound
case _ => t
}
}
}
/*
* This line search will attempt steps larger than step length one,
* unlike back-tracking line searches. It also comes with strong convergence
* properties. It selects step lengths using cubic interpolation, which
* works better than other approaches in my experience.
* Based on Nocedal & Wright.
*/
class StrongWolfeLineSearch(maxZoomIter: Int, maxLineSearchIter: Int) extends CubicLineSearch {
import scala.math._
val c1 = 1e-4
val c2 = 0.9
def minimize(f: DiffFunction[Double], init: Double = 1.0): Double = {
minimizeWithBound(f, init, bound = Double.PositiveInfinity)
}
/**
* Performs a line search on the function f with bound, returning a point satisfying
* the Strong Wolfe conditions OR satisfying sufficient decrease condition and hit bound.
* Based on the line search detailed in Nocedal & Wright Numerical Optimization p58.
* BUT add some modification for bound checking.
*/
def minimizeWithBound(f: DiffFunction[Double], init: Double = 1.0, bound: Double = 1.0): Double = {
require(init <= bound, "init value should <= bound")
def phi(t: Double): Bracket = {
val (pval, pdd) = f.calculate(t)
Bracket(t = t, dd = pdd, fval = pval)
}
var t = init // Search's current multiple of pk
var low = phi(0.0)
val fval = low.fval
val dd = low.dd
if (dd > 0) {
throw new FirstOrderException("Line search invoked with non-descent direction: " + dd)
}
/**
* Assuming a point satisfying the strong wolfe conditions exists within
* the passed interval, this method finds it by iteratively refining the
* interval. Nocedal & Wright give the following invariants for zoom's loop:
*
* - The interval bounded by low.t and hi.t contains a point satisfying the
* strong Wolfe conditions.
* - Among all points evaluated so far that satisfy the "sufficient decrease"
* condition, low.t is the one with the smallest fval.
* - hi.t is chosen so that low.dd * (hi.t - low.t) < 0.
*/
def zoom(linit: Bracket, rinit: Bracket): Double = {
var low = linit
var hi = rinit
for (i <- 0 until maxZoomIter) {
// Interp assumes left less than right in t value, so flip if needed
val t = if (low.t > hi.t) interp(hi, low) else interp(low, hi)
// Evaluate objective at t, and build bracket
val c = phi(t)
//logger.debug("ZOOM:\n c: " + c + " \n l: " + low + " \nr: " + hi)
logger.info("Line search t: " + t + " fval: " + c.fval +
" rhs: " + (fval + c1 * c.t * dd) + " cdd: " + c.dd)
///////////////
/// Update left or right bracket, or both
if (c.fval > fval + c1 * c.t * dd || c.fval >= low.fval) {
// "Sufficient decrease" condition not satisfied by c. Shrink interval at right
hi = c
logger.debug("hi=c")
} else {
// Zoom exit condition is the "curvature" condition
// Essentially that the directional derivative is large enough
if (abs(c.dd) <= c2 * abs(dd)) {
return c.t
}
// If the signs don't coincide, flip left to right before updating l to c
if (c.dd * (hi.t - low.t) >= 0) {
logger.debug("flipping")
hi = low
}
logger.debug("low=c")
// If curvature condition not satisfied, move the left hand side of the
// interval further away from t=0.
low = c
}
}
throw new FirstOrderException(s"Line search zoom failed")
}
///////////////////////////////////////////////////////////////////
for (i <- 0 until maxLineSearchIter) {
val c = phi(t)
// If phi has a bounded domain, inf or nan usually indicates we took
// too large a step.
if (java.lang.Double.isInfinite(c.fval) || java.lang.Double.isNaN(c.fval)) {
t /= 2.0
logger.error("Encountered bad values in function evaluation. Decreasing step size to " + t)
} else {
// Zoom if "sufficient decrease" condition is not satisfied
if ((c.fval > fval + c1 * t * dd) ||
(c.fval >= low.fval && i > 0)) {
logger.debug("Line search t: " + t + " fval: " + c.fval + " cdd: " + c.dd)
return zoom(low, c)
}
// We don't need to zoom at all
// if the strong wolfe condition is satisfied already.
if (abs(c.dd) <= c2 * abs(dd)) {
return c.t
}
// If c.dd is positive, we zoom on the inverted interval.
// Occurs if we skipped over the nearest local minimum
// over to the next one.
if (c.dd >= 0) {
logger.debug("Line search t: " + t + " fval: " + c.fval +
" rhs: " + (fval + c1 * t * dd) + " cdd: " + c.dd)
return zoom(c, low)
}
low = c
if (t == bound) {
logger.debug("Reach bound, satisfy sufficent decrease condition," +
" but not curvature condition satisfied.")
return bound
} else {
t *= 1.5
if (t > bound) {
t = bound
}
logger.debug("Sufficent Decrease condition but not curvature condition satisfied. Increased t to: " + t)
}
}
}
throw new FirstOrderException("Line search failed")
}
}
