# Market input and output

To read in an instance of the same-costs problem, use SameCostsMarket(f, t, h):

using OptimalApplication
mkt = SameCostsMarket(
[0.5, 0.1, 0.9, 0.7],   # admissions probabilities
[12, 20, 1, 3],         # utility values
2                       # limit on number of schools to apply to
)
SameCostsMarket{Int64}(4, [0.9, 0.7, 0.5, 0.1], [1, 3, 12, 20], 2, [3, 4, 1, 2])

The output is an instance of SameCostsMarket{U}, which contains the input data. Here U == eltype(t) and we require U <: Real.

Note

To improve the performance of the solvers, the schools are sorted in ascending order by t when the market is constructed. Therefore, mkt = SameCostsMarket(f, t, h); mkt.t == t is not necessarily true. The input data can be recovered using mkt.t == t[mkt.perm]; see SameCostsMarket(f, t, h).

To read in an instance of the varied-costs problem, use VariedCostsMarket(f, t, g, H):

mkt = VariedCostsMarket(
[0.5, 0.1, 0.9, 0.7],   # admissions probabilities
[12, 20, 1, 3],         # utility values
[3, 4, 7, 2],           # application fees
8                       # budget to spend on applications
)
VariedCostsMarket(4, [0.9, 0.7, 0.5, 0.1], [1, 3, 12, 20], [7, 2, 3, 4], 8, [3, 4, 1, 2])

The output is an instance of VariedCostsMarket. Both SameCostsMarket and VariedCostsMarket are subtypes of the abstract type Market.

Note

VariedCostsMarket supports only t with integer eltype. Because the objective function is linear in t, to work with float data, first multiply by the least common denominator.

If the application fees share a common divisor, the solvers will perform more effectively if you divide by it. For example, fees of [90, 80, 90, 70] with budget 160 is equivalent to fees of [9, 8, 9, 7] with budget 16.

OptimalApplication also provides the convenience functions Market(f, t, h) and Market(f, t, g, H), which behave just like those above.