/usr/share/pyshared/openopt/kernel/LLAVP.py is in python-openopt 0.34+svn1146-1build1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | from ooMisc import assignScript
from baseProblem import MatrixProblem
from numpy import asfarray, ones, inf, dot, nan, zeros, any, all, isfinite, eye, sign
from numpy.linalg import norm
import NSP
class LLAVP(MatrixProblem):
_optionalData = ['damp', 'X', 'c']
allowedGoals = ['minimum', 'min']
def __init__(self, *args, **kwargs):
if len(args) > 2: self.err('incorrect args number for LLAVP constructor, must be 0..2 + (optionaly) some kwargs')
if len(args) > 0: kwargs['C'] = args[0]
if len(args) > 1: kwargs['d'] = args[1]
MatrixProblem.__init__(self)
llavp_init(self, kwargs)
def objFunc(self, x):
r = norm(dot(self.C, x) - self.d, 1)
if not self.damp is None:
r += self.damp * norm(x-self.X, 1)
#if any(isfinite(self.f)): r += dot(self.f, x)
return r
def llavp2nsp(self, solver, **solver_params):
if hasattr(self,'x0'): p = NSP.NSP(ff, self.x0, df=dff)
else: p = NSP.NSP(ff, zeros(self.n), df=dff)
p.args.f = self # DO NOT USE p.args = self IN PROB ASSIGNMENT!
self.inspire(p)
self.iprint = -1
# for LLAVP plot is via NLP
p.show = self.show
p.plot, self.plot = self.plot, 0
#p.checkdf()
#p.solver.__optionalDataThatCanBeHandled__ += ['damp', 'X', 'c']
r = p.solve(solver, **solver_params)
self.xf, self.ff, self.rf = r.xf, r.ff, r.rf
return r
def _Prepare(self):
MatrixProblem._Prepare(self)
if not self.damp is None and not any(isfinite(self.X)):
self.X = zeros(self.n)
def llavp_init(prob, kwargs):
prob.probType = 'LLAVP'
prob.goal = 'minimum'
prob.allowedGoals = ['minimum', 'min']
prob.showGoal = False
kwargs['C'] = asfarray(kwargs['C'])
prob.n = kwargs['C'].shape[1]
prob.lb = -inf * ones(prob.n)
prob.ub = inf * ones(prob.n)
if 'damp' not in kwargs.keys(): kwargs['damp'] = None
if 'X' not in kwargs.keys(): kwargs['X'] = nan*ones(prob.n)
if prob.x0 is None: prob.x0 = zeros(prob.n)
return assignScript(prob, kwargs)
ff = lambda x, LLAVprob: LLAVprob.objFunc(x)
def dff(x, LLAVprob):
r = dot(sign(dot(LLAVprob.C, x) - LLAVprob.d), LLAVprob.C)
#r = (LLAVprob.C * sign(dot(LLAVprob.C, x) - LLAVprob.d)).sum(0)
if not LLAVprob.damp is None: r += LLAVprob.damp * (sign(x - LLAVprob.X)).sum(0)
return r
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