pythonpython-3.xmathematical-optimizationcvxpyconvex-optimization

Maximum Variance Unfolding with CVXPY

I am trying to reproduce the results from this paper (Weinberger and Saul, 2004, doi/10.5555/1597348.1597471), in which the authors use Maximum Variance Unfolding (MVU) to learn a low dimensional representation of a few distributions. The general problem of MVU, after dropping the rank constraint, is as follows: enter image description here Here, G is the inner product matrix of the data. Each k corresponds to a data point, and each l corresponds to one of its neighbors. Then, the e vectors are "indicator vectors" that have zeros in all entries except for the one in the index in the subscript. The second constraint zero-centers the data; I ignored this constraint in my formulation so as to keep it as simple as possible. Finally, G must be positive semidefinite.

I am trying to replicate the experiments in the paper above on a dataset which consists of 400 images (76x101 pixels) of the same progressively rotated teapot. I start by importing the data and relevant libraries:

import scipy.io
import cvxpy as cvx
import numpy as np
from sklearn.neighbors import NearestNeighbors

data = scipy.io.loadmat('teapots.mat')
data = data["Input"][0][0][0]

# make it (400 x 23028)
data = data.T.astype(np.float64)

Then, I build an adjacency matrix representation of the k-nearest neighbors of each data point:

n_points = data.shape[0]
# create sparse matrix (400 x 400) with the connections
nn = NearestNeighbors(n_neighbors=4).fit(data)
nn = nn.kneighbors_graph(data).todense()
nn = np.array(nn)

Finally, I try to perform MVU to discover low dimensional embeddings for these data using CVXPY:

# inner product matrix of the original data
X = cvx.Constant(data.dot(data.T))
# inner product matrix of the projected points; PSD constrains G to be both PSD and symmetric
G = cvx.Variable((n_points, n_points), PSD=True)
G.value = np.zeros((n_points, n_points))

# spread out points in target manifold
objective = cvx.Maximize(cvx.trace(G))
constraints = []

# add distance-preserving constraints
for i in range(n_points):
    for j in range(n_points):
        if nn[i, j] == 1:
            constraints.append(
                (X[i, i] - 2 * X[i, j] + X[j, j]) -
                (G[i, i] - 2 * G[i, j] + G[j, j]) == 0
            )

problem = cvx.Problem(objective, constraints)
problem.solve(verbose=True, max_iters=10000000)
    
print(problem.status)

However, CVXPY tells me the problem is infeasible. If I remove the constraints, the solver says the problem is unbounded, which makes sense because I'm maximizing a convex function of the decision variable. My question, then, is what mistake(s) I am making in my formulation of MVU. Thank you in advance!

I omitted, in the code above, a verification that the adjacency graph is not disconnected. To do that, I simply perform a DFS on the adjacency graph and assert that the number of visited nodes is equal to the number of rows in the data. For n=4 neighbors, this holds.

Solution

  • This is a very hard type of SDP problem actually because you have a lot of near linear dependencies. Even the best implementations will run into some sort of numerical issues and will require reduced tolerances or other tricks. It really is a bit nasty.

    Regarding your code:

    With these changes I was able to get SCS to start converging. After a while it looked like

      8500| 1.01e+00  1.11e-04  6.31e+00 -1.64e+06  5.49e-04  6.15e+02 
  8750| 1.01e+00  1.11e-04  6.39e+00 -1.64e+06  5.49e-04  6.33e+02

    I will not wait for it to finish though. You may need to fiddle with tolerances to relax them if you want it to terminate.

    I would advise you to use cvx.MOSEK (where, disclamer, I work) as a solver, but it seems the CVXPY compilation procedure to MOSEK runs into some issues and starts eating a lot of RAM taking forever. So instead here is a direct implementation in MOSEK Fusion API (with the rescaling and normalizing constraint):

    from mosek.fusion import *
import scipy.io
import numpy as np
from sklearn.neighbors import NearestNeighbors
import sys

data = scipy.io.loadmat('teapots.mat')
data = data["Input"][0][0][0]

# make it (400 x 23028)
data = data.T.astype(np.float64)

n_points = data.shape[0]
# create sparse matrix (400 x 400) with the connections
nn = NearestNeighbors(n_neighbors=4).fit(data)
nn = nn.kneighbors_graph(data).todense()
nn = np.array(nn)

# inner product matrix of the original data
X = data.dot(data.T)/10**6
print(X)
# inner product matrix of the projected points; PSD constrains G to be both PSD and symmetric
M = Model()
G = M.variable(Domain.inPSDCone(n_points))

M.constraint(Expr.sum(G), Domain.equalsTo(0))

# spread out points in target manifold
M.objective(ObjectiveSense.Maximize, Expr.sum(G.diag()))

# add distance-preserving constraints
for i in range(n_points):
    for j in range(n_points):
        if nn[i, j] == 1:
            M.constraint(Expr.add([G.index([i,i]),G.index([j,j]),Expr.mul(-2,G.index([i,j]))]), Domain.equalsTo(X[i, i] - 2 * X[i, j] + X[j, j]))

# Print log
M.setLogHandler(sys.stdout)

# Reduce accuracy
M.setSolverParam("intpntCoTolRelGap", 1e-5)
M.setSolverParam("intpntCoTolPfeas",1e-5)
M.setSolverParam("intpntCoTolDfeas",1e-5)

#Solve
M.solve()

Gresult = G.level().reshape((n_points,n_points))
print(Gresult)

    The solution looks OK:

    Interior-point solution summary
  Problem status  : PRIMAL_AND_DUAL_FEASIBLE
  Solution status : OPTIMAL
  Primal.  obj: 1.5209452618e+06    nrm: 4e+03    Viol.  con: 2e-02    var: 0e+00    barvar: 0e+00  
  Dual.    obj: 1.5209449262e+06    nrm: 8e+03    Viol.  con: 0e+00    var: 0e+00    barvar: 1e-04