def print_results(alg, x, time2run):
print("===results summary for " + alg + "===")
print("running time " + str(time2run))
print(alg + " training error: " + str(gp.training_error_rate(A, x, y)))
print(alg + " nnz: " + str(sum(abs(x) > 1e-5)))
group_norms = gp.nnz_groups(Partitions, x)
print(alg + " nnz groups: " + str(sum(group_norms > 1e-5)))
print("=== end results summary===")
print('##################')
print('##################')
print("running ada3op")
init = algo.InitPoint([], [], np.zeros(d + 1), [])
out3op = algo.adap3op(group_prox,
theGrad_smart,
lrFunc,
theFunc,
prox_L1,
lrFunc_smart,
init,
stepIncrease=step_increase,
iter=iter,
lip_const=lam1,
gamma=initial_stepsize)
print_results("ada3op", out3op.x, out3op.times[-1])
print("running 1fbt...")
init = algo.InitPoint(np.zeros(d + 1), np.zeros(d + 1), np.zeros(d + 1),
maxIter = 10000
algos2run = ['ps2fembed_g']
numTrialPoints = 13
start = -6
end = 6
trials = np.logspace(start,end,numTrialPoints)
results = {}
i = 0
for tuned in trials:
print(f"tuned = {tuned}")
if False:
init = algo.InitPoint([],np.zeros(d),np.zeros(d),np.zeros(p))
out1f = algo.PS1f_bt_comp(init,maxIter,G,theProx1,theProx2,
theGrad,Gt,theFunc,gamma = tuned,
equalRhos=False,getFuncVals=False,verbose=False)
results[(i,'1f')] = out1f.finalFuncVal
if False:
init = algo.InitPoint([],[],np.zeros(d),np.zeros(p))
out2f = algo.PS2f_bt_comp(init,int(0.5*maxIter),G,theProx1,theProx2,theGrad,Gt,
theFunc,gamma=tuned,equalRhos=False,getFuncVals=False,verbose=False)
results[(i,'2f')] = out2f.finalFuncVal
if False:
init = algo.InitPoint(np.zeros(d),np.zeros(p),[],[])
outcp = algo.cpBT(theProx2, theGradSmart, proxg, theFunc, hfunc, init, iter=maxIter,
beta=tuned,stepInc=1.1,K=Gt,Kstar=G,verbose=False,getFuncVals=False)
print("running cvx...")
tstart_cvx = time.time()
[cvxopt, xopt] = algo.runCVX_portfolio(d, Q, m, r)
tend_cvx = time.time()
print("CVX runtime: " + str(tend_cvx - tstart_cvx))
print("=========================")
print("Running projective splitting one-forward-step backtrack... (ps1fbt)")
# initPoint returns an initial point to start the algorithm.
# initPoint is called before every algorithm to ensure we get new memory for
# each new variable
initPoint = algo.InitPoint(
np.ones(d) / d,
np.ones(d) / d,
np.ones(d) / d, np.zeros(d))
out1f = algo.PS1f_bt(theFunc,
theProx1,
theProx2,
theGrad,
initPoint,
gamma=gamma1f,
hyper_resid=hyper_resid,
verbose=verbose,
iter=iter,
adaptive_gamma=False)
print_results("1fbt", out1f.times[-1], out1f.x1, out1f.grad_evals)
print("=========================")