HANSO and HIFOO Two New MATLAB Codes - PowerPoint PPT Presentation

Title: HANSO and HIFOO Two New MATLAB Codes


1
HANSO and HIFOOTwo New MATLAB Codes

• Michael L. Overton
• Courant Institute of Mathematical Sciences
• New York University
• Singapore, Jan 2006

2
Two New Codes

• HANSO
  Hybrid Algorithm for Nonsmooth
  Optimization
• Aimed at finding local minimizers of general
  nonsmooth, nonconvex optimization problems
• Joint with Jim Burke and Adrian Lewis
• HIFOO
  H-Infinity Fixed-Order
  Optimization
• Aimed at solving specific nonsmooth, nonconvex
  optimization problems arising in control
• Built on HANSO
• Joint with Didier Henrion

3
Many optimization objectives are

• Nonconvex
• Nonsmooth
• Continuous
• Differentiable almost everywhere
• With gradients often available at little
  additional cost beyond that of computing function
• Subdifferentially regular
• Sometimes, non-Lipschitz

4
Numerical Optimization of Nonsmooth, Nonconvex
Functions

• Steepest descent jams
• Bundle methods better, but these are mainly
  intended for nonsmooth convex functions
• We developed a simple method for nonsmooth,
  nonconvex minimization based on Gradient Sampling
• Intended for continuous functions that are
  differentiable almost everywhere, and for which
  the gradient can be easily computed when it is
  defined
• User need only write routine to return function
  value and gradient and need not worry about
  nondifferentiable cases, e.g., ties for a max
  when coding the gradient
• Very recently, found BFGS is often very effective
  when implemented correctly - and much less
  expensive

5
BFGS

• Standard quasi-Newton method for optimizing
  smooth functions, using gradient differences to
  update an inverse Hessian matrix H, defining a
  local quadratic model of the objective function
• Conventional wisdom jams on nonsmooth functions
• Amazingly, works very well as long as implemented
  right (weak Wolfe line search is essential)
• It builds an excellent, extremely ill-conditioned
  quadratic model
• Often, runs until cond(H) is 1016 before breaking
  down, taking steplengths of around 1
• Often converges linearly (not superlinearly)
• By contrast, steepest descent and Newtons method
  usually jam
• Not very reliable, and no convergence theory

6
(No Transcript)
7
(No Transcript)
8
(No Transcript)
9
Minimizing a Product of Eigenvalues

• An application due to Anstreicher and Lee
• Min log of product of K largest eigenvalues of A
  o X
• subject to X positive semidefinite and diag(X)1
• (A, X symmetric) (we usually set KN/2)
• Would be equivalent to SDP if replace product by
  sum
• Number of variables is n N(N-1)/2 where N is
  dimension of X
• Following Burer, substitute Y Y for X where Y is
  N by r so n is reduced to rN and normalize Y so
  its rows have norm 1 thus both constraints
  eliminated
• Typically objective is not smooth at minimizer,
  because the K-th largest eigenvalue is multiple
• Results for N10 and rank r 2 through 10

10
(No Transcript)
11
N10, r6 eigenvalues of optimal A o X

• 3.163533558166873e-001
• 5.406646569314501e-001
• 5.406646569314647e-001
• 5.406646569314678e-001
• 5.406646569314684e-001
• 5.406646569314689e-001
• 5.406646569314706e-001
• 7.149382062194134e-001
• 7.746621702661097e-001
• 1.350303124150612e000

12
The U and V Spaces

• The condition number of H, the BFGS approximation
  to inverse Hessian, typically reaches 1016 !!
• Lets look at eigenvalues of H
• H QDQ ( denotes transpose)
• Search direction is d -Hg -QDQg (where g
  gradient)
• Next plot shows
• diag(D), sorted into ascending order
• Components of Qg, using same ordering
• Components of DQg, using same ordering
  (search direction
  expanded in eigenvector basis)
• Tiny eigenvalues of H correspond to nonsmooth
  V-space
• Other eigenvalues of H correspond to smooth
  U-space
• From matrix theory, dim(V-space) m(m1)/2 1,
  where m is multiplicity of Kth eigenvalue of
  A o X

13
(No Transcript)
14
(No Transcript)
15
More on the U and V Spaces

• H seems to be an excellent approximation to the
  limiting inverse Hessian and evidently gives us
  bases for the optimal U and V spaces
• Lets look at plots of f along random directions
  in the U and V spaces, as defined by eigenvectors
  of H, passing through minimizer (for the N10,
  r6 example)

16
(No Transcript)
17
(No Transcript)
18
(No Transcript)
19
Line Search for BFGS

• Sufficient decrease
• for 0 lt c1 lt 1,
• f (x t d) f (x) c1 t (grad f)(x)d
• Weak Wolfe condition on derivative
• for c1 lt c2 lt 1,
• (grad f)(x t d)d c2 (grad f)(x)d
• Not strong Wolfe condition on derivative
• (grad f)(x t d)d - c2 (grad f)(x)d
• Essential when f is nonsmooth
• No reason to use strong Wolfe even if f is smooth
• Much simpler to implement
• Why ever use strong Wolfe?

20
But how to check if final x is locally optimal?

• Extreme ill-conditioning of H is a strong clue,
  but is expensive to compute and proves nothing
• If x is locally optimal, running a local bundle
  method from x establishes nonsmooth stationarity
  this involves repeated null-step line searches
  and building a bundle of gradients obtained via
  the line searches
• When line search returns null step, it must also
  return gradient at a point lying across the
  discontinuity
• Then new d arg min d d Î conv G ,
  where G is the set of gradients in the bundle
• Terminate if d is small
• If d is 0 and line search steps were 0, x is
  Clarke stationary more realistically it is
  close to Clarke stationary
• Ill call this Clarke Quay Stationary since it is
  a Quay Idea

21
First-order local optimality

• Is this implied by Clarke stationarity of f at x
  ?
• No, for example f(x) -x is Clarke stationary
  at 0
• Yes, in sense that f(x d) 0 for all
  directions d, when f is regular at x
  (subdifferentially regular, Clarke regular)
• Most of the functions that we have studied are
  regular everywhere, although this is sometimes
  hard to prove
• Regularity generalizes smoothness and convexity

22
Harder Problems

• Eigenvalue product problems are interesting, but
  the Lipschitz constant L for f is not large
• Harder problems
• Chebyshev Exponential Approximation
• Optimization of Distance to Instability
• Pseudospectral Abscissa Optimization (arbitarily
  large L)
• Spectral Abscissa Optimization (not even
  Lipschitz)
• In all cases, BFGS turns out to be substantially
  less reliable than gradient sampling

23
Gradient Sampling Algorithm Initialize e and x.
Repeat

• Get G, a set of gradients of function f evaluated
  at x and at points near x (sampling controlled by
  e)
• Let d arg min d d Î conv G
• Line search replace x by x t d, with
  f(x t d) lt f(x)
  (if d is not 0)

until d 0 (or d tol) Then reduce e
and repeat.
24
Convergence Theory for Gradient Sampling Method
(SIOPT, 2005)

• Suppose
• f is locally Lipschitz and coercive
• f is continuously differentiable on an open dense
  subset of its domain
• number of gradients sampled near each iterate is
  greater than problem dimension
• line search uses an appropriate sufficient
  decrease condition
• Then, with probability one and for fixed sampling
  parameter e, algorithm generates a sequence of
  points with a cluster point x that is e-Clarke
  stationary
• If f has a unique Clarke stationary point x, then
  the set of all cluster points generated by the
  algorithm converges to x as e is reduced to zero
• Kiwiel has already improved on this! (For a
  slightly different, as yet untested, version of
  the algorithm.)

25
(No Transcript)
26
(No Transcript)
27
(No Transcript)
28
HANSO

• User provides routine to compute f and its
  gradient at any given x (do not worry about
  nonsmooth cases)
• BFGS is then run from many randomly generated or
  user-provided starting points. Quit if gradient
  at best point found is small.
• Otherwise, a local bundle method is run from best
  point found, attempting to verify local
  stationarity.
• If this is not successful, gradient sampling is
  initiated.
• Regardless, return a final x along with a set of
  nearby points, the corresponding gradients, and
  the d which is the smallest vector in the convex
  hull of these gradients
• If the nearby points are near enough and d is
  small enough, f is Clarke Quay Stationary at x
• This is an approximate first-order local
  optimality condition if f is regular at x

29
Optimization in Control

• Often, not many variables, as in low-order
  controller design
• Reasons
• engineers like simple controllers
• nonsmooth, nonconvex optimization problems in
  even a few variables can be very difficult
• Sometimes, more variables are added to the
  problem in an attempt to make it more tractable
• Example adding a Lyapunov matrix variable P and
  imposing stability on A by AP PA 0
• When A depends linearly on the original
  parameters, this results in a bilinear matrix
  inequality (BMI), which is typically difficult to
  solve
• Our approach tackle the original problem
• Looking for locally optimal solutions

30
H? Norm of a Transfer Function

• Transfer function G(s)C (sI A)-1 B D
• SS representation
• H? norm is ? if A is not stable (stable means all
  eigenvalues have negative real part) and
  otherwise, sup G(s)2 over all imaginary s
• Standard way to compute it is norm(SS,inf) in
  Matlab control toolbox
• LINORM from SLICOT is much faster

31
H? Fixed-Order Controller Design

• Choose matrices a, b, c, d to minimize the H?
  norm of the transfer function
• The dimension of a is k by k (order, between 0
  and ndim(A))
• The dimension of b is k by p (number of system
  inputs)
• The dimension of c is m (number of system
  outputs) by k
• The dimension of d is m by p
• Total number of variables is (km)(kp)

32
H? Fixed-Order Controller Design

• Choose matrices a, b, c, d to minimize the H?
  norm of the transfer function
• The dimension of a is k by k (order, between 0
  and ndim(A))
• The dimension of b is k by p (number of system
  inputs)
• The dimension of c is m (number of system
  outputs) by k
• The dimension of d is m by p
• Total number of variables is (km)(kp)
• The case k0 is static output feedback

33
H? Fixed-Order Controller Design

• Choose matrices a, b, c, d to minimize the H?
  norm of the transfer function
• The dimension of a is k by k (order, between 0
  and ndim(A))
• The dimension of b is k by p (number of system
  inputs)
• The dimension of c is m (number of system
  outputs) by k
• The dimension of d is m by p
• Total number of variables is (km)(kp)
• The case k0 is static output feedback
• When B1,C1 are empty, all I/O channels are in
  performance measure

34
HIFOO H? Fixed-Order Optimization

• Aims to find a, b, c, d for which H? norm is
  locally optimal
• Begins by minimizing the spectral abscissa
  max(real(eig(A-block))) until finds a, b, c, d
  for which A-block is stable (and therefore H?
  norm is finite)
• Then locally minimizes H? norm
• Calls HANSO to carry out both optimizations
• Alternative objectives
• Optimize the spectral abscissa instead of
  quitting when stable
• Optimize the pseudospectral abscissa
• Optimize the distance to instability (complex
  stability radius)
• The output a,b,c,d can then be input to optimize
  for larger order k, which cannot give worse
  result
• Accepts various input formats

35
HIFOO Provides the Gradients

• For H? norm, it combines
• left and right singular vector info at point on
  imaginary axis where sup achieved
• chain rule
• For spectral abscissa max(real(eig)), it combines
• left and right eigenvector info for eigenvalue
  achieving max real part
• chain rule
• Do not have to worry about ties
• Function is continuous, but gradient is not
• However, function is differentiable almost
  everyhere (and virtually everywhere that it is
  evaluated)
• Typically, not differentiable at an exact
  minimizer

36
Benchmark Examples AC Suite

• Made extensive runs for the AC suite of 18
  aircraft control problems in Leibfritz COMPLeIB
• In many cases we found low-order controllers that
  had smaller H? norm than was supposedly possible
  according to the standard full-order controller
  MATLAB routine HINFSYN!
• Sometimes this was even the case when the order
  was set to 0 (static output feedback)
• After I announced this at the SIAM Control
  meeting in July, HINFSYN was extensively debugged
  and a new version has been released

37
Benchmark Examples Mass-Spring

• A well known simple example with n4
• Optimizing spectral abscissa
• Order 1, we obtain 0, known to be optimal value
• Order 2, we obtain about -0.73, previously
  conjectured to be about -0.5

38
Benchmarks Belgian Chocolate Challenge

• Simply stated stabilization problem due to
  Blondel
• From 1994 to 2000, not known if solvable by any
  order controller
• In 2000, an order 11 controller was discovered
• We found an order 3 controller, and using our
  analytical results, proved that it is locally
  optimal

39
Availability of HANSO and HIFOO

• Version 0.9, documented in a paper submitted to
  ROCOND 2006, freely available at
• http//www.cs.nyu.edu/overton/software/hifoo/
• Version 0.91, available online but not fully
  tested so no public link yet
• Version 0.92 will have further enhancements and
  we hope to announce it widely in a month or two

40
Some Relevant Publications

• A Robust Gradient Sampling Algorithm for
  Nonsmooth, Nonconvex Optimization
• SIOPT, 2005 (with Burke, Lewis)
• Approximating Subdifferentials by Random Sampling
  of Gradients
• MOR, 2002 (with Burke, Lewis)
• Stabilization via Nonsmooth, Nonconvex
  Optimization
• submitted to IEEE Trans-AC (with Burke, Henrion,
  Lewis)
• HANSO A Hybrid Algorithm for Non-Smooth
  Optimization Based on BFGS, Bundle and Gradient
  Sampling
• in planning stage
• http//www.cs.nyu.edu/faculty/overton/

41

• ????
• Gong xi fa cai!
• Gung hei fat choy!
• ????