547 lines
18 KiB
Julia
547 lines
18 KiB
Julia
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using LinearAlgebra, Printf, Plots
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function NCG(f;
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x::Union{Nothing, Vector}=nothing,
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wf::Integer=0,
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rstart::Integer=0,
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eps::Real=1e-6,
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astart::Real=1,
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MaxFeval::Integer=1000,
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m1::Real=0.01,
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m2::Real=0.9,
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tau::Real=0.9,
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sfgrd::Real=0.2,
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MInf::Real=-Inf,
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mina::Real=1e-16,
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plt::Union{Plots.Plot, Nothing}=nothing,
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plotatend::Bool=false,
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Plotf::Integer=0,
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printing::Bool=true)
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#function [ x , status ] = NCG( f , x , wf , rstart , eps , astart ,
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# MaxFeval , m1 , m2 , tau , sfgrd , MInf ,
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# mina )
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#
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# Apply a Nonlinear Conjugated Gradient algorithm for the minimiztion of
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# the provided function f, which must have the following interface:
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#
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# [ v , g ] = f( x )
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#
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# Input:
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#
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# - x is either a [ n x 1 ] real (column) vector denoting the input of
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# f(), or [] (empty).
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#
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# Output:
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#
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# - v (real, scalar): if x == [] this is the best known lower bound on
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# the unconstrained global optimum of f(); it can be -Inf if either f()
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# is not bounded below, or no such information is available. If x ~= []
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# then v = f(x).
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#
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# - g (real, [ n x 1 ] real vector): this also depends on x. if x == []
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# this is the standard starting point from which the algorithm should
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# start, otherwise it is the gradient of f() at x (or a subgradient if
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# f() is not differentiable at x, which it should not be if you are
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# applying the gradient method to it).
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#
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# The other [optional] input parameters are:
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#
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# - x (either [ n x 1 ] real vector or [], default []): starting point.
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# If x == [], the default starting point provided by f() is used.
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#
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# - wf (integer scalar, optional, default value 0): which of the Nonlinear
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# Conjugated Gradient formulae to use. Possible values are:
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# = 0: Fletcher-Reeves
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# = 1: Polak-Ribiere
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# = 2: Hestenes-Stiefel
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# = 3: Dai-Yuan
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#
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# - rstart (integer scalar, optional, default value 0): if > 0, restarts
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# (setting beta = 0) are performed every n * rstart iterations
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#
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# - eps (real scalar, optional, default value 1e-6): the accuracy in the
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# stopping criterion: the algorithm is stopped when the norm of the
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# gradient is less than or equal to eps. If a negative value is provided,
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# this is used in a *relative* stopping criterion: the algorithm is
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# stopped when the norm of the gradient is less than or equal to
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# (- eps) * || norm of the first gradient ||.
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#
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# - astart (real scalar, optional, default value 1): starting value of
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# alpha in the line search (> 0)
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#
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# - MaxFeval (integer scalar, optional, default value 1000): the maximum
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# number of function evaluations (hence, iterations will be not more than
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# MaxFeval because at each iteration at least a function evaluation is
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# performed, possibly more due to the line search).
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#
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# - m1 (real scalar, optional, default value 0.01): first parameter of the
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# Armijo-Wolfe-type line search (sufficient decrease). Has to be in (0,1)
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#
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# - m2 (real scalar, optional, default value 0.9): typically the second
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# parameter of the Armijo-Wolfe-type line search (strong curvature
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# condition). It should to be in (0,1); if not, it is taken to mean that
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# the simpler Backtracking line search should be used instead
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#
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# - tau (real scalar, optional, default value 0.9): scaling parameter for
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# the line search. In the Armijo-Wolfe line search it is used in the
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# first phase: if the derivative is not positive, then the step is
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# divided by tau (which is < 1, hence it is increased). In the
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# Backtracking line search, each time the step is multiplied by tau
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# (hence it is decreased).
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#
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# - sfgrd (real scalar, optional, default value 0.2): safeguard parameter
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# for the line search. to avoid numerical problems that can occur with
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# the quadratic interpolation if the derivative at one endpoint is too
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# large w.r.t. the one at the other (which leads to choosing a point
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# extremely near to the other endpoint), a *safeguarded* version of
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# interpolation is used whereby the new point is chosen in the interval
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# [ as * ( 1 + sfgrd ) , am * ( 1 - sfgrd ) ], being [ as , am ] the
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# current interval, whatever quadratic interpolation says. If you
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# experiemce problems with the line search taking too many iterations to
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# converge at "nasty" points, try to increase this
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#
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# - MInf (real scalar, optional, default value -Inf): if the algorithm
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# determines a value for f() <= MInf this is taken as an indication that
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# the problem is unbounded below and computation is stopped
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# (a "finite -Inf").
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#
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# - mina (real scalar, optional, default value 1e-16): if the algorithm
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# determines a stepsize value <= mina, this is taken as an indication
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# that something has gone wrong (the gradient is not a direction of
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# descent, so maybe the function is not differentiable) and computation
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# is stopped. It is legal to take mina = 0, thereby in fact skipping this
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# test.
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#
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# Output:
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#
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# - x ([ n x 1 ] real column vector): the best solution found so far.
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#
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# - status (string): a string describing the status of the algorithm at
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# termination
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#
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# = 'optimal': the algorithm terminated having proven that x is a(n
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# approximately) optimal solution, i.e., the norm of the gradient at x
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# is less than the required threshold
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#
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# = 'unbounded': the algorithm has determined an extrenely large negative
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# value for f() that is taken as an indication that the problem is
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# unbounded below (a "finite -Inf", see MInf above)
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#
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# = 'stopped': the algorithm terminated having exhausted the maximum
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# number of iterations: x is the bast solution found so far, but not
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# necessarily the optimal one
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#
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# = 'error': the algorithm found a numerical error that prevents it from
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# continuing optimization (see mina above)
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#
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#{
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# =======================================
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# Author: Antonio Frangioni
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# Date: 10-11-22
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# Version 1.21
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# Copyright Antonio Frangioni
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# =======================================
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#}
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# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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# inner functions - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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function f2phi(alpha, derivate=false)
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# computes and returns the value of the tomography at alpha
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#
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# phi( alpha ) = f( x + alpha * d )
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#
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# if Plotf > 2 saves the data in gap() for plotting
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#
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# if the second output parameter is required, put there the derivative
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# of the tomography in alpha
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#
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# phi'( alpha ) = < \nabla f( x + alpha * d ) , d >
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#
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# saves the point in lastx, the gradient in lastg and increases feval
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lastx = x + alpha * d
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phi, lastg, _ = f(lastx)
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if Plotf > 2
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if fStar > -Inf
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push!(gap, (phi - fStar) / max(abs(fStar), 1))
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else
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push!(gap, phi)
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end
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end
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feval += 1
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if derivate
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return (phi, dot(d, lastg))
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end
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return (phi, nothing)
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end
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# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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function ArmijoWolfeLS(phi0, phip0, as, m1, m2, tau)
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# performs an Armijo-Wolfe Line Search.
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#
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# phi0 = phi( 0 ), phip0 = phi'( 0 ) < 0
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#
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# as > 0 is the first value to be tested: if phi'( as ) < 0 then as is
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# divided by tau < 1 (hence it is increased) until this does not happen
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# any longer
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#
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# m1 and m2 are the standard Armijo-Wolfe parameters; note that the strong
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# Wolfe condition is used
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#
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# returns the optimal step and the optimal f-value
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lsiter = 1 # count iterations of first phase
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local phips, phia
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while feval ≤ MaxFeval
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phia, phips = f2phi(as, true)
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if (phia ≤ phi0 + m1 * as * phip0) && (abs(phips) ≤ -m2 * phip0)
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if printing
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@printf("\t%2d", lsiter)
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end
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a = as
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return (a, phia) # Armijo + strong Wolfe satisfied, we are done
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end
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if phips ≥ 0 # derivative is positive, break
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break
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end
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as = as / tau
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lsiter += 1
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end
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if printing
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@printf("\t%2d ", lsiter)
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end
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lsiter = 1 # count iterations of second phase
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am = 0
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a = as
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phipm = phip0
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while (feval ≤ MaxFeval ) && (as - am) > mina && (phips > 1e-12)
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# compute the new value by safeguarded quadratic interpolation
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a = (am * phips - as * phipm) / (phips - phipm)
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a = max(am + ( as - am ) * sfgrd, min(as - ( as - am ) * sfgrd, a))
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# compute phi(a)
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phia, phip = f2phi(a, true)
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if (phia ≤ phi0 + m1 * a * phip0) && (abs(phip) ≤ -m2 * phip0)
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break # Armijo + strong Wolfe satisfied, we are done
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end
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# restrict the interval based on sign of the derivative in a
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if phip < 0
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am = a
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phipm = phip
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else
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as = a
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if as ≤ mina
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break
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end
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phips = phip
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end
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lsiter += 1
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end
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if printing
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@printf("%2d", lsiter)
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end
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return (a, phia)
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end
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# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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function BacktrackingLS(phi0, phip0, as, m1, tau)
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# performs a Backtracking Line Search.
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#
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# phi0 = phi( 0 ), phip0 = phi'( 0 ) < 0
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#
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# as > 0 is the first value to be tested, which is decreased by
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# multiplying it by tau < 1 until the Armijo condition with parameter
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# m1 is satisfied
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#
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# returns the optimal step and the optimal f-value
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lsiter = 1 # count ls iterations
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while feval ≤ MaxFeval && as > mina
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phia, _ = f2phi(as)
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if phia ≤ phi0 + m1 * as * phip0 # Armijo satisfied
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break # we are done
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end
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as *= tau
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lsiter += 1
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end
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if printing
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@printf("\t%2d", lsiter)
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end
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return (as, phia)
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end
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# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Plotf = 1;
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# 1 = the level sets of f and the trajectory are plotted (when n = 2)
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# 2 = the function value / gap are plotted, iteration-wise
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# 3 = the function value / gap are plotted, function-evaluation-wise
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# all the rest: nothing is plotted
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Interactive = false # if we pause at every iteration
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PXY = Matrix{Real}(undef, 2, 0)
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local gap
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if Plotf > 1
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if Plotf == 2
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MaxIter = 50 # expected number of iterations for the gap plot
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else
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MaxIter = 70 # expected number of iterations for the gap plot
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end
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gap = []
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end
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if Plotf == 2 && plt == nothing
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plt = plot(xlims=(0, MaxIter), ylims=(1e-15, 1e+1))
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end
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if Plotf > 1 && plt == nothing
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plt = plot(xlims=(0, MaxIter))
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end
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if plt == nothing
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plt = plot()
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end
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# reading and checking input- - - - - - - - - - - - - - - - - - - - - - - -
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# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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local fStar
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if isnothing(x)
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fStar, x, _ = f(nothing)
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else
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fStar, _, _ = f(nothing)
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end
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n = size(x, 1)
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if wf < 0 || wf > 3
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error("unknown NCG formula %d", wf)
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end
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if astart ≤ 0
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error("astart must be > 0")
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end
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if m1 ≤ 0 || m1 ≥ 1
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error("m1 is not in (0 ,1)")
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end
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AWLS = ( m2 > 0 && m2 < 1 )
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if tau ≤ 0 || tau ≥ 1
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error("tau is not in (0 ,1)")
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end
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if sfgrd ≤ 0 || sfgrd ≥ 1
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error("sfgrd is not in (0, 1)")
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end
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if mina < 0
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error("mina is < 0")
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end
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# "global" variables- - - - - - - - - - - - - - - - - - - - - - - - - - - -
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# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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lastx = zeros(n, 1) # last point visited in the line search
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lastg = zeros(n, 1) # gradient of lastx
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pastg = zeros(n, 1)
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pastd = zeros(n, 1)
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d = zeros(n, 1) # NGC's direction
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feval = 0 # f() evaluations count ("common" with LSs)
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status = "error"
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# initializations - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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if printing
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@printf("NCG method ")
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if wf == 0
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@printf("(Fletcher-Reeves)\n")
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elseif wf == 1
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@printf("(Polak-Ribiere)\n")
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elseif wf == 2
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@printf("(Hestenes-Stiefel)\n")
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elseif wf == 3
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@printf("(Dai-Yuan)\n")
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end
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if fStar > -Inf
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@printf("feval\trel gap")
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else
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@printf("feval\tf(x)")
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end
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@printf("\t\t|| g(x) ||\tbeta\tls feval\ta*\n\n")
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end
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v, _ = f2phi(0)
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ng = norm(lastg)
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if eps < 0
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ng0 = -ng # norm of first subgradient: why is there a "-"? ;-)
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else
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ng0 = 1 # un-scaled stopping criterion
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end
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iter = 1 # iterations count (as distinguished from f() evals)
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# main loop - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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while true
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# output statistics - - - - - - - - - - - - - - - - - - - - - - - - - -
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if fStar > - Inf
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gapk = (v - fStar) / max(abs(fStar), 1)
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if printing
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@printf("%4d\t%1.4e\t%1.4e", feval, gapk, ng)
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end
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if Plotf == 2
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push!(gap, gapk)
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end
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else
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if printing
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@printf("%4d\t%1.8e\t\t%1.4e", feval, v, ng)
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end
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if Plotf == 2
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push!(gap, v)
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end
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end
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# stopping criteria - - - - - - - - - - - - - - - - - - - - - - - - - -
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if ng ≤ eps * ng0
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status = "optimal"
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break
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end
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if feval > MaxFeval
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status = "stopped"
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break
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end
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# compute search direction- - - - - - - - - - - - - - - - - - - - - - -
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# formulae could be streamlined somewhat and some norms could be saved
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# from previous iterations
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if iter == 1 # first iteration is off-line, standard gradient
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d = -lastg
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if printing
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@printf("\t")
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end
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else # normal iterations, use appropriate NCG formula
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if rstart > 0 && mod(iter, n * rstart) == 0
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# ... unless a restart is being prformed
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beta = 0
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if printing
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@printf("\t(res)")
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end
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else
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if wf == 0 # Fletcher-Reeves
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beta = (ng / norm(pastg))^2
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elseif wf == 1 # Polak-Ribiere
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beta = (dot(lastg, (lastg - pastg))) / norm(pastg)^2
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beta = max(beta, 0)
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elseif wf == 2 # Hestenes-Stiefel
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beta = (dot(lastg, (lastg - pastg))) / (dot((lastg - pastg), pastd))
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if beta < 0
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beta = 0
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end
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elseif wf == 3 # Dai-Yuan
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beta = ng^2 / (dot((lastg - pastg), pastd) )
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end
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if printing
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@printf("\t%1.4f", beta)
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end
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end
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if beta ≠ 0
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d = -lastg + beta * pastd
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else
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d = -lastg
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end
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end
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pastg = lastg # previous gradient
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pastd = d # previous search direction
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# compute step size - - - - - - - - - - - - - - - - - - - - - - - - - -
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phip0 = dot(lastg, d)
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local a
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if AWLS
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a, v = ArmijoWolfeLS(v, phip0, astart, m1, m2, tau)
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else
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a, v = BacktrackingLS(v, phip0, astart, m1, tau)
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end
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# output statistics - - - - - - - - - - - - - - - - - - - - - - - - - -
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if printing
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@printf("\t%1.2e\n", a)
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end
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if a ≤ mina
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status = "error"
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break
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end
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if v ≤ MInf
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||
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status = "unbounded"
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break
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end
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# compute new point - - - - - - - - - - - - - - - - - - - - - - - - - -
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# possibly plot the trajectory
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if n == 2 && Plotf == 1
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PXY = hcat(PXY, hcat(x, lastx))
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end
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x = lastx
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ng = norm(lastg)
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# iterate - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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||
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||
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iter += 1
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||
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if Interactive
|
||
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readline()
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end
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end
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# end of main loop- - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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||
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if Plotf ≥ 2
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plot!(plt, gap)
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elseif Plotf == 1 && n == 2
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plot!(plt, PXY[1, :], PXY[2, :])
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end
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if plotatend
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display(plt)
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||
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end
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||
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return (x, status)
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||
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end # the end- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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