I think in the private sector, when you are given a problem, when you are, let's say, in a private firm, in a bank, or a law firm, or any professional firm, you're given a set of problems, perhaps a client has a certain problem, and your role is to help solve that problem.
The students were given a set of problems to solve starting with very straightforward ones and gradually getting more challenging.
Given a set of features, the problem is selecting a subset that leads to the least classification error.
Given a set of celestial bodies, the problem of finding an optimal sequence of swing-bys, deep space manoeuvres (DSM) and transfer arcs connecting the elements of the set is combinatorial in nature.
Given a set of asset classes, the problem concerns the definition and management over time of the best asset mix to achieve favorable returns subject to various uncertainties, policy and legal constraints, and other requirements.
Given a set of options constituting a decision problem, decision theory recommends an option that maximizes utility, that is, an option whose utility equals or exceeds the utility of every other option.
Given a set of cities, the Traveling Salesman Problem (TSP) is to find an ordering of cities that minimizes the total length of the tour when visiting all the cities in some order and returning to the starting city.
We deal with the in-silico chemogenomics problem as the following machine learning problem: given a set of n compound-protein pairs (C1, P1),..., (C n, P n), then estimate a function f(C, P) that would predict whether a compound C binds to a protein P. In addition, we attempt to estimate an interpretable function f in order to extract informative features.
The Common Motifs Problem is as follows [ 26]: Common Motifs Problem Given a set of N sequences S i (1 ≤ i ≤ N) and two integers e ≥ 0 and 2 ≤ q ≤ N, where e is the number of errors allowed and q is the required quorum, find all models m that appear in at least q distinct sequences of S i.
We then discuss a more problem - given a set of configurations on the vertices of a graph (for example colorings), how do we sample from that?
Given a set of strings, the longest common nonsuperstring problem is finding the longest string that is not a superstring of any string in the given set.
