2012 06-01 V_0.0.5 Krisz/Youyi
* There is a branching after 0.0.5. Krisz developed 0.0.6- to test various
quadratic optimizers and working set selection strategies to improve
convergence of the quadratic optimization problem.
2012-06-25 0.0.6
* Add init.alpha
* Implement 'PBB' quadratic optimizer. 'PBB' was later discarded for 'tron'
* Add optimizers 'tron' quadratic optimizer
* Add working set selection strategies 'v','v2','rv','rvwr','rv2wg','rvwg'
2012-07-06 0.0.7 Krisz
* Develop 'exhaustive' quadratic optimizer. Added interfaces to get the inverse
of a matrix by LU-factorization and by Moore-Penrose inverse from LAPACK
2012-07-25 0.0.8 Krisz
* Add 'hideo' quadratic optimizer from SVM_LIGHT implementation
2012-08-14 V_0.0.5 Youyi/Krisz
* developed 0.1.5- to add small improvements while keeping the original
optimizer (minQuad) and original working set selection strategy (greedy).
2012-08-14 V_0.1.5 Youyi
* Add init.alpha
2012-10-08 V_0.1.7 Krisz
* Add mem.efficient - add get_Qx C function and replace all Q %*% x calls in rauc by a
call to get_Qx. get_Qx uses Shuxin's indexing to obtain entries in 'Q_{n1n2 x n1n2}' from
entries in K_{n1+n2 x n1+n2}. Note that get_Qx does not implement roll-out sums (LAPACK)
or KAHAN summing and thus is less accurate than R's crossprod(). However we do not expect
to run this algorithm on n > 500 hence n1n2 is no greater than ~ 250,000.
2012-10-08 V_0.1.8 Krisz/Youyi
* define intCEIL
* V_0.0.5 shows more warning about minQuad not converged than V_0.1.8.
This is probably due to the fact that minQuad tolerance is set to 1e-7 in
V_0.0.5 and 1e-4 in V_0.1.8.
* copy getK from svmw and remove dependency on svmw
* add vcov.gridsearch, sim.NL1, remove sim.MH12
2012-10-16 V_0.1.9 Krisz
* Removed all VLA declarations (to improve scalability) whose size ~ n,n^2
in every function that accesses the matrices 'Q' or 'K' (minQuad(),get_Q(),..)
One can check logic by uncommenting '#define DO_VLA' and recompiling
There are still VLA declarations for 2 2x2 matrices and 1 4x2 matrix and few small arrays of length 2, that is ok
* Added Kahan-summing via '#define DO_KAHAN' for mem.efficient = T/F
note the 'volatile' keyword:
http://stackoverflow.com/questions/72552/why-does-volatile-exist - someone actually mentions Kahan sum
http://stackoverflow.com/questions/246127/why-is-volatile-needed-in-c - an example x + h - x
* added on.exit() to minQuad.R to free memory in case R session is interrupted
(alternatively could request dynamic memory from R's heap by
'#defining' CALLOC as in package 'rpart')
* removed all function calls from the 'main' while() loop in minQuad.c
* VLA without KAHAN summing may be faster by as much as 25% based on few runs with sim.dat.1
* Fixed a bug in minQuad that existed since V_0.1.7. if one did not specify n1 = 0, n2 = 0 explicitely even if
mem.efficient = FALSE (the default n1 = NULL, n2 = NULL) then a SEGV can.will occur in .C())
* Added mem.efficient input argument to minQuad()
* Modified rauc_2.R according to change in minQuad's interface
Modified minQuad.Rd file together with its examples
* Modified rauc.Rd file slightly
* mem.efficient defaults to TRUE in rauc() but FALSE in minQuad()
* updated minQuad.Rd help file and changed argument of minQuad to 'H'
* added 'control' interface to minQuad()
* recompiled minQuad with Kahan-summing if mem.efficient = TRUE to be able to compare it to qp-optimizer 'TRON' in 'aucmx' for n = 300
2012-11-29 V_0.1.9 Krisz
* aucm_0.1.9 was slower than 0.1.8 because I removed/commented out 'if statements'
that check if 'x == 0.0', which avoids further computation. I removed these statements
when updating 'Ha = H %*% alpha' for H = K,Q in minQuad.c and also in the function get_Qx(), because I started
aucm_0.1.9 from scratch to make a cleaner code.
* differences from V_0.1.8:
* It does not make any user-defined function calls in the main loop
* It does not compute the restricted objective function unless it has to
* I changed the order of the loops in get_Qx() to skip the inner loop if 'x == 0.0'
2012-12-11 V_0.1.10 Krisz
* Combined V_0.1.5 'default' optimizer with 'tron','hideo', and 'exhaustive' optimizers.
R's minQuad calls minQuad_2.c if 'default' is specified or minQuad.c if any of the
other optimizers are specified or if working set size 'q' > 2
* Added 'dynamic = T/F' to select size of working set size q = sqrt(# of violators) at each
minQuad iteration
* Added get_Q_pred C function to get linear.combination in rauc() and avoid declaring
npred x n1n2 matrix in R. No KAHAN-summing or roll-out summing is implemented for this.
* call to gc() by default before rauc() returns
2012-12-11 V_0.1.11 Krisz
* remove dymanic = T/F but make it the default if optimizer = 'tron'
In that case working set size is NULL and a '0' is passed to C
* updated help file minQuad.Rd, created control.minQuad.Rd
* changed the 'optimizer' argument name to 'method' in control.minQuad
(it'd be more familiar to users if we made minQuad's interface as similar
as possible to that of optim())
2012-12-11 V_0.1.12 Youyi
* Add sdca functions
* fix qp_solvers.c:40:errno undeclared on linux
* re-introduce youyi's code in v-0.1.8
* fixed a bug in sim.dat.1, it changes the simulation results
* add partial auc maxi to rauc function
2012-12-26 V_0.1.13 Youyi
* remove pAUC code since that is not right
* modify dtron to pass function pointers to update functions in
* introduce auc class, which is parent of sauc, rauc, gridsearch, and
sdca and rlogit, add coef and predict for auc
* add test code to ?aucm packag help page
2013-01-07 V_0.1.14 Krisz
* fixed the get_Q_pred_x function by reverting to my original code
(the one underneath it that is commented out).
* warning messages in C code:
* The loqo. warnings were easy/quick fixes.
* The hideo.c warning is a little worrisome: The logic assumes 0 <= b1,b2 < n and thus g0_b1,g0_b2 will be initialized but the compiler does not
know that assumption. Hopefully it is correct. I added 2 initialization lines at lines 243,372 and 395
but I am not spending any time on testing it or understanding why this un-initialization is so.
The code is no worse than it was before this change.
'dtrpcg' accepts an int* 'iters' which is used as a loop counter and the caller, not the callee,
uses the value computed (aside from being used as a loop counter) so I do not understand why the
compiler complains and it does worry me a little bit as it should not complain:
dtrpcg.c: In function 'dtrpcg':
dtrpcg.c:157:26: warning: value computed is not used [-Wunused-value]
At line 157 I made the change:
*iters++ -> (*iters)++
Now it does not complain.
The message below has no easy fix. The function 'dprecond' returns a double but that double is not used
in dspcg. So the authors simply store the value so that all compilers compile it, at the expense of this
warning in windows for this compiler:
dspcg.c: In function 'dspcg':
dspcg.c:113:40: warning: variable 'alpha' set but not used [-Wunused-but-set-variable]
2013-01-10 V_0.1.14 Youyi
* modifies tron source code to allow for convex optimization
* sauc.dca() code in R for linear kernel
* sauc_dca_tron() C interface to tron for linear kernel
2013-01-17 V_0.1.15 Krisz/Youyi
* completed sauc.dca() that Youyi made
* sauc.dca() code in R for linear and nonlinear kernels using optim() or tron()
* sauc_dca_tron() C interface to tron for linear and nonlinear kernel
* change sauc.phi so that the default max.it is 1e3 and even if not
converging, parameter estimate from the last iteration is returned
* change BYlogreg so that even when not converged, the combination
from the last iteration is returned
2013-02-07 V_0.1.15 Krisz
* adjusted the function value returned by minQuad when solveQuad was specified as solveQuad uses the parametrization
a'Qa - b'a whereas minQuad uses 0.5a'Q'+b'a (divided the returned function value of solveQuad and minQuad_2 by 2.0)
2013-04-04 V_0.1.16 Krisz
* added ibs_kernel.c and R function ibs.kernel() that calls C code and
R_ibs() function, an R implementation.
* added DXD() function to matrix.c
* added the function 'matprod' to matrix.c
* Experimental:
* individual upper-bounds on variables (weighted svm) to be used by
'hideo' and 'loqo' but something is wrong - both optimizers fail
* added to control.minQuad the 'NAOK' option to .C() call
* we must set NAOK = TRUE in calls to from minQuad(in R) to minQuad(in C)
this is because - take the variable 'value' = 0.5*a'Ha + b'a
if DUP = FALSE and a previous call to minQuad resulted in a NaN value to be returned,
the next call to minQuad(R) will pass value=NaN to minQuad(C), even though the R script
"tries" to set 'value = as.double(0)'. In C it will be set to 0.0, but never makes it there
due to NAOK = FALSE. But do not want NAOK = TRUE by default since we do not process
NAs in minQuad.
2013-04-04 V_0.1.17 Youyi
* sdca work
2013-07-24 V_0.1.18 Youyi
* implement decomposition algorithm for sdca
* replace s with 1/s in rauc so that s is the same as described in paper
2013-08-20 V_0.1.20 Krisz
* minQuad (R): - renamed argument 'working.set' to 'ws' and ret.wset to ret.ws, changed .Rd files
* - loqo: added arguments lower,upper,mat.constr,lhs.constr,rhs.constr
* hideo: removed
* added get_sub_matrix() to matrix.c which can handle row or column-major input and output matrices in any combination. separated working_set.c into:
* sort.h/sort.c // revsort
* random.h/random.c // sampling routines + rank()
* working_set.h/working_set.c // only working set selection routines
* Now one only need to include "working_set.h" in the sauc-related .c files
* added the 'static' keyword for all global variables in sauc-related files and qp_solver.c . A better way to do this can be found in MutRSeq package.
* loqo algorithmically starts from an infeasible point of its own choosing (infeasible with respect to the simple bound constraints lower and upper) at q = 2 and does not get away from that point. Forcing a pre-specified starting point or bounding the solution to [lower,upper] does not seem to help.
2013.12-10 Youyi
* change default value for initwml to FALSE in BYlogreg
2015.12-28 Youyi
* rename BYlogreg to rlogit and remove dependence on rrcov
* sim.dat.1 changed between 0.1.20 and aucm_2013.12-10
*
Todolist
* let auc.dca scale each predictor by default