In particular, here we show an example using Chimera. One can instantiate the Scalarizer object using the following code. Assume an optimization problem consists of parameter space X ∈ R d with d continuous parameters and an objective space Y ∈ R n with n objectives, corresponding to objective functions f = {f i } n i=1 : X → Y. After the k th optimization iteration, the scalarize method of the Scalarizer object takes as input a set of objective measurements {y i } k i=1 and applies the ASF, s : Y → [0, 1], to the current observations, returning a set of scalarvalued merits {ỹ i } k i=1 . Conventionally, lower merit values correspond to a more optimal experiment. The scalarized dataset of observations D = {(x i , ỹi )} k i=1 is then passed to the experiment planning algorithm in order for the next parameter recommendations to be computed. Algorithm 1 shows pseudocode for multi-objective optimization with ASFs using the lower-level "ask-tell" interface of Olympus. Alternatively, the higher-level Evaluator interface for multi-objective ASF optimization is depicted in the code snippet below. After instantiating the Scalarizer object, it is passed to the Evaluator, along with the emulator, campaign and experiment planner instances. Effectively, the Evaluator abstracts away the "ask-tell" operations associated with an optimization experiment. The new version of Olympus ships with an updated suite of analytic benchmark surfaces to support the new discrete and categorical parameter types. Namely, we include 5 fully-categorical surfaces, cat camel, cat michalewicz, cat dejong, cat slope and cat ackley. The parameter dimensionality and number of options per categorical variable can be userspecified. Fully-categorical surfaces are explained in greater detail and visualized in SI Sec. S.2 and SI Fig. , respectively. Also, given the software's extension to include multi-objective campaigns, we provide wrappers for 5 analytical benchmark surfaces with multiple objectives. Specifically, wrappers for the mult fonseca, mult viennet, mult zdt1, mult zdt2, mult zdt3 functions are provided, which comprise 2, 3, 2, 2, and 2 objectives, respectively. The multi-objective surfaces are described in further detail in SI Sec. S.2 and visualized in SI Fig. .