max(), min(), sum() and prod() accept a plain number before an expression. max(0, demand - stock) used to stop with "invalid 'type' (list) of argument", because base R chooses the method of these functions by their first argument only; only max(demand - stock, 0) worked. quicopt now provides its own versions of the four functions, which pass any call without an expression to base R unchanged. R notes them as masked from base when the package is attached.num_var() and int_var() take semi = TRUE for a variable that is either 0 or between its bounds, never in between: a delivery of nothing or of 10 to 25 units. Its bounds must be finite. active(x) is 1 when such a variable is not 0, so a fee per delivery is fee * active(delivery), and a model that is otherwise linear is still solved to a proven optimum.block(m, name, vars) names the variables of one subproblem, such as one customer's deliveries. Every solution the service returns satisfies the constraints over one block's variables alone. A variable is in at most one block and needs finite bounds.part_var(m, "teams", n, parts) declares a partition: each of n items in one of parts parts. part_of(item, P) is an item's part and size_of(part, P) the number of items in a part, both vectorized over their first argument. A solution reports both under res$structures$<name>, and set_start(), evaluate() and resample() carry the division along.max(), min(), holds() or a switch is solved by a search; such a model whose variables are all integer or binary needs finite bounds on each, as before, but one with a continuous variable is now accepted.partition_decl(), the constants SEMICONTINUOUS and SEMIINTEGER, and program(blocks = ) are the data-layer forms. The service must be recent enough to know these constructs; an older one refuses the model when it is sent.solve() and submit() take time_limit, the number of seconds the solve may run, in place of the time the service would choose. A solver that proves its answer sooner still stops sooner; a search runs for all of it. The service grants at most its maximum, and a warning says so when it granted less; res$solver_data$time_budget_s is the time it granted. solve() waits for the answer at least time_limit plus a minute, and job_result() polls that long by default. The service must be recent enough to know the setting; an older one refuses the request.perm_var(m, "tour", n) declares a permutation: n items in n slots, one each. item_at(slot, P) is the item in a slot and slot_of(item, P) the slot of an item, integer expressions vectorized over their first argument; precede(P, a, b) requires item a in an earlier slot than item b. A solution reports both views under res$structures$<name>, and set_start(), evaluate() and resample() carry the order along with the plain variables.lookup_table(m, "dist", values) declares a numeric vector or matrix whose entries are read at positions the solver decides: dist[item_at(1:4, tour), item_at(2:5, tour)] is the legs of a round, cost[choice] a cost chosen through an integer variable. A lookup is an ordinary expression, and is random exactly when one of its positions is.permutation_decl(), ir_struct_ref() and ir_table_ref() are the data-layer forms, and program() takes structures.uniform(min, max), exponential(rate) and bernoulli(prob) join normal() as named distributions, parameterized like runif(), rexp() and rbinom(n, 1, prob). As with normal(), a parameter may be an expression, so a rate or a failure probability can depend on a decision. A numeric parameter outside its range is refused where the distribution is built.variance(x) and std_dev(x) measure how much a quantity varies over the scenarios. By default the scenarios are the whole distribution, so variance(x) is expectation(x^2) - expectation(x)^2; sample = TRUE divides by n - 1 as var() and sd() do.scenario_max(x) and scenario_min(x) are the largest and the smallest value over the scenarios, and scenario_quantile(x, prob) is the value that the share prob of scenarios stays at or below (quantile(type = 1) of the scenario values). Minimizing scenario_max(cost) optimizes the worst scenario of the sample.The release for checking a solution, not only finding one.
resample(m, res, seed) evaluates a solution on scenarios it was not
optimized for, and reports the out-of-sample objective and whether every
constraint still holds. evaluate(m, res, expr) gives the value of any
non-random expression at a solution, on the model's scenarios or, with
seed, on fresh ones.add(m, prob(...) >= level, margin = k) tightens a chance constraint's level
by k standard errors of the scenario estimate, so the level is cleared out
of sample rather than merely in sample.holds(a <= b) is a comparison as a 0/1 expression, in each scenario; all six
comparisons are allowed there, with an optional tolerance.add(m, rel, when = b) imposes a row only where the binary b is 1.set_start(m, res) warm-starts the next solve from a previous solution;
bin_var() takes a start too.rand_var(m, "weight", normal(c(6, 5, 4), 1)) declares a vector random
variable, one independent draw per element.is_random(x) says whether an expression still varies across scenarios. An
aggregator applied to a non-random expression, and a random objective or
constraint, are now refused in R with the public names, before the service
refuses them with wire names.mean() of a model expression is the mean over its elements, like sum();
c() concatenates expressions.Fixed:
-x and prod() over three or more elements encoded shapes the service
rejected.x <= 3 with a vector x added one row instead of one per element.solution of a result is ordered as the variables were declared. It
used to keep the order the answer arrived in, which is the service's own,
so which(res$solution == 1) did not count items the way the model did.Documentation: every help topic with a usage section now states its value.
First release.