/*------------------------------------------------------------------------
* gettime - Get xinu time in seconds past Jan 1, 1970
*------------------------------------------------------------------------
*/
status gettime(
uint32 *timvar /* Location to store the result */
)
{
uint32 now; /* Current time (UCT) */
int32 retval; /* Return value from call */
/* Get current time in UCT representation (GMT) */
retval = getutime(&now);
if (retval == SYSERR) {
return SYSERR;
}
/* Adjust to xinu time and store result */
*timvar = utim2ltim(now);
return OK;
}
/*------------------------------------------------------------------------
* gettime - get local time in seconds past Jan 1, 1970
*------------------------------------------------------------------------
*/
status gettime(
uint32 *timvar /* location to store the result */
)
{
uint32 now; /* current time (UCT) */
int32 retval; /* return value from call */
/* Get current time in UCT representation (GMT) */
retval = getutime(&now);
if (retval == SYSERR) {
return SYSERR;
}
/* Adjust to local time and store result */
*timvar = utim2ltim(now) + (Date.dt_daylight ? SECPERHR: 0);
return OK;
}
static void test_getutime() {
long long t1 = getutime();
long long t2 = getutime();
CU_ASSERT_TRUE(t2 >= t1);
}
double bundle_solve (bundle_t *bundle, int max_iterations, int max_qp_iterations, double subgnorm_opt_tol, double linerr_opt_tol, double z_cutoff,
void *data, double (*bundle_callback) (void*, double*, double*),
double init_scale, double acceptable_model_exactness, double *init_x, double init_z, double *init_subg)
/*
* Maximizes a convex non-differentiable function using the bundle method, until reaching a value or iteration threshold (z_cutoff and max_iterations, respectively), or until fulfilling optimality criteria (see subgnorm_opt_tol and linerr_opt_tol):
* - max_qp_iterations: maximum enumerations of mask to solve QP
* - subgnorm_opt_tol: threshold below which a subgradient norm are deemed to be zero,
* - linerr_opt_tol: threshold below which a linearization error is deemed to be zero,
* - z_cutoff: threshold above which z no longer needs to be maximized any further,
* - data: a pointer passed on to bundle_callback (see bundle_step),
* - init_scale: initial QP penalty,
* - acceptable_model_exactness: if the ratio between actual and predicted improvement is above this value, then the bundle method performs a major step,
* - init_x: initial values for best_x, may be NULL in which case 0 is used,
* - init_z: initial value for best_z, ignored if init_x or init_subg are NULL,
* - init_subg: subgradient at init_x, may be NULL.
*/
{
int i;
bundle_status_t status;
// initialize the bundle
if (NULL == init_x) {
for (i = 0; i < bundle->n; i++)
bundle->best_x[i] = 0.0;
bundle->actual_z = bundle_callback(data, bundle->best_x, &bundle->a[0]);
}
else {
memcpy(bundle->best_x, init_x, bundle->n * sizeof(double));
if (NULL == init_subg) {
bundle->actual_z = bundle_callback(data, bundle->best_x, &bundle->a[0]);
}
else {
memcpy(&bundle->a[0], init_subg, bundle->n * sizeof(double));
bundle->actual_z = init_z;
}
}
bundle->max_z = bundle->best_z = bundle->actual_z;
memcpy(bundle->max_x, bundle->best_x, bundle->n * sizeof(double));
memcpy(bundle->best_subg, &bundle->a[0], bundle->n * sizeof(double));
bundle->m = 1;
bundle->most_recent_i = 0;
bundle->b[0] = bundle->actual_z;
bundle->aat[0] = ddot(bundle->n, &bundle->a[0], &bundle->a[0]);
bundle->scale = init_scale;
bundle->time_bundle = bundle->time_qp = bundle->time_callback = 0.0;
//printf("it: 0\tpen: %f\tmax: %f\n", bundle->scale, bundle->actual_z);
// find a maximum within max_iterations or until greater than z_cutoff
bundle->time_bundle -= getutime(1);
for (bundle->n_iterations = 1; bundle->n_iterations < max_iterations; bundle->n_iterations++) {
// perform bundle step
status = bundle_step(bundle, max_qp_iterations, subgnorm_opt_tol, linerr_opt_tol, z_cutoff, data, bundle_callback, init_scale, acceptable_model_exactness);
// check for termination criteria
if (bundle_status_cutoff == status || bundle_status_optimal == status || bundle_status_tolerably_optimal == status)
break;
}
bundle->time_bundle += getutime(1);
// terminate the bundle search
return bundle->max_z;
}
bundle_status_t bundle_step (bundle_t *bundle, int max_qp_iterations, double subgnorm_opt_tol, double linerr_opt_tol, double z_cutoff, void* data, double (*bundle_callback) (void*, double*, double*), double init_scale, double acceptable_model_exactness)
/*
* Performs one iteration of the bundle method:
* - max_qp_iterations: maximum enumerations of mask to solve QP
* - subgnorm_opt_tol: threshold below which a subgradient norm are deemed to be zero,
* - linerr_opt_tol: threshold below which a linearization error is deemed to be zero,
* - z_cutoff: threshold above which z no longer needs to be maximized any further,
* - data: a pointer passed on to bundle_callback,
* - double bundle_callback(void *data, double *x, double *subg): must evalute and return z at x, writing the subgradient at x in subg.
* - init_scale: initial penalty parameter
* - acceptable_model_exactness: if the ratio between actual and predicted improvement is above this value, then the bundle method performs a major step,
*/
{
int new_subg_i = -1;
double roh;
double previous_best_z = bundle->best_z;
double *new_subg = NULL;
double subg_delta, linerr_opt_delta, agg_subg_square;
int i;
//printf("it: %i\tpen: %f\t", bundle->n_iterations, bundle->scale);
// guess where the x yielding the optimal z lies
bundle->time_qp -= getutime(1);
bundle->guessed_z = bundle_guess(bundle, max_qp_iterations);
bundle->time_qp += getutime(1);
linerr_opt_delta = bundle->agg_b - bundle->best_z;
agg_subg_square = ddot(bundle->n, bundle->agg_subg, bundle->agg_subg);
//printf("z-est: %f ", bundle->guessed_z);
// update the bundle
new_subg_i = bundle_update(bundle, agg_subg_square);
// evaluate guessed x and subgradient at guessed x
new_subg = &bundle->a[new_subg_i * bundle->n];
bundle->time_callback -= getutime(1);
bundle->actual_z = bundle_callback(data, bundle->x, new_subg);
bundle->time_callback += getutime(1);
// update maximums
if (bundle->actual_z > bundle->max_z) {
bundle->max_z = bundle->actual_z;
memcpy(bundle->max_x, bundle->x, bundle->n * sizeof(double));
if (bundle->max_z >= z_cutoff) {
//printf("(%f)*\tcutoff\n", bundle->actual_z);
return bundle_status_cutoff;
}
}
//printf("(%f)%c\t", bundle->actual_z, (bundle->actual_z == bundle->max_z) ? '*' : ' ');
// update A.A^T with new subgradient
for (i = 0; i < bundle->m; i++)
bundle->aat[i * bundle->max_m + new_subg_i] = bundle->aat[new_subg_i * bundle->max_m + i] = ddot(bundle->n, &bundle->a[i * bundle->n], new_subg);
// check for optimality, i.e. if new subgradient is a null vector
if (bundle->aat[new_subg_i * bundle->max_m + new_subg_i] < bundle->epsilon) {
//printf("optimal\n");
return bundle_status_optimal;
}
//printf("agg.err: %f\tagg.norm: %f\t", linerr_opt_delta, sqrt(agg_subg_square));
if (linerr_opt_delta <= linerr_opt_tol && agg_subg_square <= subgnorm_opt_tol * subgnorm_opt_tol) {
//printf("optimal withing tolerances\n");
return bundle_status_tolerably_optimal;
}
// check improvement
roh = (bundle->actual_z - previous_best_z) / (bundle->guessed_z - previous_best_z + bundle->epsilon);
//printf("roh: %.2f\t", roh);
if (roh >= acceptable_model_exactness) {
// Major Step
// update penalization parameter bundle->scale
daxpy(bundle->n, -1.0, new_subg, bundle->best_subg);
subg_delta = ddot(bundle->n, bundle->best_subg, bundle->best_subg);
bundle->scale = (subg_delta == 0.0) ? init_scale : (1.0 / (1.0 / bundle->scale + ddot(bundle->n, bundle->kkt_x, bundle->best_subg) / subg_delta));
// center the penalized QP to new maximum
bundle->best_z = bundle->actual_z;
memcpy(bundle->best_x, bundle->x, bundle->n * sizeof(double));
memcpy(bundle->best_subg, new_subg, bundle->n * sizeof(double));
// update QP rhs for new center
memcpy(bundle->b, bundle->next_b, bundle->max_m * sizeof(double));
bundle->b[new_subg_i] = bundle->actual_z;
// the most recent subgradient may not be active
bundle->most_recent_i = -1;
//printf("major step\n");
return bundle_status_major_step;
}
else {
// Minor Step
// update rhs for new subgradient for existing center
bundle->b[new_subg_i] = bundle->actual_z - ddot(bundle->n, new_subg, bundle->kkt_x);
//printf("\n");
// the most recent subgradient is certainly active
bundle->most_recent_i = new_subg_i;
return bundle_status_minor_step;
}
}
double bundle_guess (bundle_t* bundle, int max_qp_iterations)
/*
* Guesses where the x giving the estimated best z lies by solving the penalized bundle QP.
*
* If the resolution of the QP takes more than max_qp_iterations mask guesses, it is aborted,
* and the bundle is repopulated as if it were aggregated in the previous bundle iteration,
* in other words reduced to only 2 subgradients, the previous aggregate and the previous evaluated subgradient.
* The QP is then solved anew, and completely.
* The procedure then computes x, the aggregate subgradient and the linearization error for this QP.
*
*/
{
int solved;
int i, n_iter = max_qp_iterations;
bundle->time_qp -= getutime(1);
bundle->kkt_z = INFINITY;
solved = bundle_qp_solve(bundle, 0ULL, bundle->m, &n_iter);
if (!solved) {
bundle->time_qp += getutime(1);
++bundle->n_iterations;
//printf("trim\nit: %i\tpen: %f\t", bundle->n_iterations, bundle->scale);
// trim a
if (bundle->m != 2)
memcpy(&bundle->a[bundle->n], &bundle->a[(bundle->m - 1) * bundle->n], bundle->n * sizeof(double));
memcpy(&bundle->a[0], bundle->agg_subg, bundle->n * sizeof(double));
// recompute aat
bundle->aat[0] = ddot(bundle->n, bundle->agg_subg, bundle->agg_subg);
bundle->aat[bundle->max_m + 1] = bundle->aat[(bundle->m - 1) * bundle->max_m + bundle->m - 1];
bundle->aat[1] = bundle->aat[bundle->max_m] = ddot(bundle->n, bundle->agg_subg, &bundle->a[bundle->n]);
// trim b
bundle->b[0] = (bundle->most_recent_i == -1) ? bundle->agg_next_b : bundle->agg_b;
bundle->b[1] = bundle->b[bundle->m - 1];
// update bundle information and solve again
bundle->most_recent_i = -1;
bundle->m = 2;
bundle->time_qp -= getutime(1);
n_iter = (max_qp_iterations < 3) ? 3 : max_qp_iterations;
bundle->kkt_z = INFINITY;
solved = bundle_qp_solve(bundle, 0ULL, bundle->m, &n_iter);
assert(solved);
}
// compute the aggregate subgradient using BLAS (in bundle->agg_subg[])
bundle->agg_b = bundle->agg_next_b = 0.0;
bzero(bundle->agg_subg, bundle->n * sizeof(double));
for (i = 0; i < bundle->kkt_m; i++) {
daxpy(bundle->n, bundle->kkt_mul[i], &bundle->a[bundle->kkt_i[i] * bundle->n], bundle->agg_subg);
bundle->agg_b += bundle->kkt_mul[i] * bundle->b[bundle->kkt_i[i]];
bundle->agg_next_b += bundle->kkt_mul[i] * bundle->next_b[bundle->kkt_i[i]];
}
// compute local x
memcpy(bundle->kkt_x, bundle->agg_subg, bundle->n * sizeof(double));
dscal(bundle->n, 1.0 / bundle->scale, bundle->kkt_x);
// compute global x
memcpy(bundle->x, bundle->best_x, bundle->n * sizeof(double));
daxpy(bundle->n, 1.0, bundle->kkt_x, bundle->x);
bundle->time_qp += getutime(1);
// return guessed z
return bundle->kkt_mul[bundle->kkt_m];
}
//------------------------------------------------------------------------
// rwhod - Periodically clean cache and (optionally) send rwho packets
//------------------------------------------------------------------------
PROCESS
rwhod(void)
{
int i, j;
struct rwent *rwptr;
struct rwent *myptr;
struct rwhopac *rpacptr;
struct rw_who *rwwptr;
struct epacket *packet;
IPaddr mynet;
long now;
int len;
int ps;
// Initialize rwho information
Rwho.rwnent = 1;
Rwho.rwsend = TRUE;
getutime(&Rwho.rwbtime);
myptr = &Rwho.rwcache[0];
getname(myptr->rwmach, RMACLEN);
myptr->rwboot = myptr->rwlast = myptr->rwslast = Rwho.rwbtime;
for (i = 0; i < 3; i++)
myptr->rwload[i] = 0L;
myptr->rwusers = 1;
getnet(mynet);
for (; TRUE; sleep(RWDELAY)) {
getutime(&now);
myptr->rwlast = myptr->rwslast = now;
ps = disable();
for (i = 0; i < Rwho.rwnent; i++) {
rwptr = &Rwho.rwcache[i];
if (now - rwptr->rwlast > RWMAXDT) {
Rwho.rwnent--;
for (j = i--; j < Rwho.rwnent; j++)
Rwho.rwcache[j] =
Rwho.rwcache[j + 1];
}
}
restore(ps);
if (!Rwho.rwsend)
continue;
packet = (struct epacket *)getbuf(Net.netpool);
rpacptr = (struct rwhopac *)
((struct udp *)
(((struct ip *)packet->ep_data)->i_data))->u_data;
rpacptr->rw_vers = RWVERSION;
rpacptr->rw_type = RWSTATUS;
rpacptr->rw_sndtim = hl2net(now);
rpacptr->rw_rtim = 0L;
getname(rpacptr->rw_host, sizeof(rpacptr->rw_host));
for (j = 0; j < RWNLOAD; j++)
rpacptr->rw_load[j] = 0L;
rpacptr->rw_btim = hl2net(Rwho.rwbtime);
len = RWMINP;
if (marked(Shl.shmark) && Shl.shused) {
rwwptr = &rpacptr->rw_rww[0];
strlcpy(rwwptr->rw_tty, "Console", RWNLEN);
strncpy(rwwptr->rw_nam, Shl.shuser, RWNLEN);
rwwptr->rw_ton = hl2net(Shl.shlogon);
rwwptr->rw_idle = hl2net(now - Shl.shlast);
len += sizeof(struct rw_who);
}
udpsend(mynet, URWHO, URWHO, packet, len);
}
}
