Function Reference on cvxoptdes

ethanol

Mon, 01 Jan 0001 00:00:00 +0000

Description

This dataset reports 88 measurements from an experiment in which ethanol was burned in a single cylinder automobile test engine. The data originates from the SemiPar package, which is now archived.

Usage

ethanol

Format

A data.frame containing 88 measurements with columns:

NOx: numeric, concentration of nitric oxide (NO) and nitrogen dioxide (NO2) in engine exhaust, normalized by the work done by the engine
C: numeric, the compression ratio of the engine
E: numeric, the equivalence ratio at which the engine was run – a measure of the richness of the air/ethanol mix

Source

Brinkman, N.D. (1981). Ethanol fuel – a single-cylinder engine study of efficiency and exhaust emissions. SAE transactions Vol. 90, No 810345, 1410–1424.

fullerenes

Mon, 01 Jan 0001 00:00:00 +0000

Description

This dataset reports the production of o-xylenyl adducts of Buckminster fullerenes. Three process conditions (temperature, reaction time and ratio of sultine to C60) are varied to maximize the mole fraction of the desired ’ product.

Usage

fullerenes

Format

A data.frame containing 120 experiments with columns:

reaction_time: numeric, reaction time in flow reactor (min)
sultine_amount: numeric, relative concentration of sultine to C60
temperature: numeric, temperature of the reaction (Celsius)
product_amount: numeric, mole fraction of the desired product

Source

Olympus: Fullerenes dataset

gam_design

Mon, 01 Jan 0001 00:00:00 +0000

Description

An R6 class for managing penalized locally optimal experiment designs for generalized additive models. This class encapsulates the generalized additive model at a set of nominal parameter values, the discrete design grid, optimization of an approximate continuous design, augmentation of an existing design, and optimal rounding to exact integer designs.

Optimal design problem

The following penalized optimization problem is solved by $optimize():

w^* \ = \ \arg \max_{w} \Phi(M(w | X, v, \theta, F, g) + \lambda I) - \alpha \cdot c^T w

where,

glm_design

Mon, 01 Jan 0001 00:00:00 +0000

Description

An R6 class for managing penalized locally optimal experiment designs for generalized linear models. This class encapsulates the generalized linear model at a set of nominal parameter values, the discrete design grid, optimization of an approximate continuous design, augmentation of an existing design, and optimal rounding to exact integer designs.

Optimal design problem

The following penalized optimization problem is solved by $optimize():

w^* \ = \ \arg \max_{w} \Phi(M(w | X, v, \theta, F, g) + \lambda I) - \alpha \cdot c^T w

where,

imidazoles

Mon, 01 Jan 0001 00:00:00 +0000

Description

This dataset reports the yield of the desired product and the reaction cost in a direct arylation reaction of imidazoles. Two continuous process conditions (temperature, initial concentration) and three reaction molecules (base, ligand, solvent) are varied to maximize the yield of the desired product while minimizing the cost of the reaction.

Usage

imidazoles

Format

A data.frame containing 1728 experiments with columns:

yield: numeric, experimental yield of the reaction (%)
cost: numeric, cost of the reaction scaled to the unit interval
concentration: numeric, initial substrate concentration (M)
temperature: numeric, temperature of the reaction (Celsius)
base: character, common name of the reaction base molecule
base_smiles: character, SMILES string of the reaction base molecule
solvent: character, common name of the reaction solvent molecule
solvent_smiles: character, SMILES string of the reaction solvent molecule
ligand: character, common name of the reaction ligand molecule
ligand_smiles: character, SMILES string of the reaction ligand molecule

Source

References

Shields B.J. et al. (2021) “Bayesian reaction optimization as a tool for chemical synthesis”, Nature, Vol. 590 (7844). Torres J.A.G. et al. (2022) “A Multi-Objective Active Learning Platform and Web App for Reaction Optimization”, J. Am. Chem. Soc., Vol. 144 (43).

lm_design

Mon, 01 Jan 0001 00:00:00 +0000

Description

An R6 class for managing penalized optimal experiment designs for linear models. This class encapsulates the linear model formula, the discrete design grid, optimization of an approximate continuous design, augmentation of an existing design, and optimal rounding to exact integer designs.

Optimal design problem

The following penalized optimization problem is solved by $optimize():

w^* \ = \ \arg \max_{w} \Phi(M(w | X, v) + \lambda I) - \alpha \cdot c^T w

where,

lme_design

Mon, 01 Jan 0001 00:00:00 +0000

Description

An R6 class for managing penalized optimal experiment designs for random intercept linear mixed models. This class encapsulates a random intercept linear mixed model, the discrete design grid, optimization of an approximate continuous design, augmentation of an existing design, and optimal rounding to exact integer designs including split-plot designs.

Optimal design problem

The following penalized optimization problem is solved by $optimize():

w^* \ = \ \arg \max_{w} \Phi(M(w | X, Z, \eta, v) + \lambda I) - \alpha \cdot c^T w

where,

nlm_design

Mon, 01 Jan 0001 00:00:00 +0000

Description

An R6 class for managing penalized locally optimal experiment designs for nonlinear models. This class encapsulates the nonlinear model at a set of nominal parameter values, the discrete design grid, optimization of an approximate continuous design, augmentation of an existing design, and optimal rounding to exact integer designs.

Optimal design problem

The following penalized optimization problem is solved by $optimize():

w^* \ = \ \arg \max_{w} \Phi(M(w | X, v, \theta) + \lambda I) - \alpha \cdot c^T w

where,

scs_control_dflt

Mon, 01 Jan 0001 00:00:00 +0000

Description

Allow the user to tune the parameters of the SCS solver procedure.

Usage

scs_control_dflt(
 maxiter = 1e+05,
 eps_rel = 1e-05,
 eps_abs = 1e-05,
 eps_infeas = 1e-07,
 alpha_relax = 1.5,
 rho_x = 1e-06,
 scale = 0.1,
 normalize = TRUE,
 acceleration_lookback = 10,
 acceleration_interval = 5,
 adaptive_scale = TRUE,
 adaptive_diag_scale = TRUE,
 time_limit_secs = 60
)

Arguments

maxiter: positive integer, maximum number of iterations to run.
eps_rel: positive numeric, relative feasibility tolerance.
eps_abs: positive numeric, absolute feasibility tolerance.
eps_infeas: positive numeric, infeasibility tolerance (primal and dual).
alpha_relax: numeric value in (0, 2), douglas-Rachford relaxation parameter.
rho_x: positive numeric, primal scale factor.
scale: positive numeric, initial dual scale factor, updated if adaptive_scale is TRUE.
normalize: logical, whether to perform heuristic data rescaling.
acceleration_lookback: positive integer, relative amount of memory used for Anderson acceleration. More memory requires more time to compute but can give more reliable steps. If acceleration_lookback < 1, Anderson acceleration is disabled.
acceleration_interval: positive integer, run Anderson acceleration every acceleration_interval iteration(s).
adaptive_scale: logical, whether to heuristically adapt dual scale through the solve.
adaptive_diag_scale: logical, whether to refine the metric R per row, using the primal residual profile. Requires adaptive_scale (silently disabled without it).
time_limit_secs: positive numeric, time limit for solve run in seconds (can be fractional). The value 0 is interpreted as no limit. The default is 60.

Function Reference on cvxoptdes

ethanol

Description

Usage

Format

Source

fullerenes

Description

Usage

Format

Source

gam_design

Description

Optimal design problem

glm_design

Description

Optimal design problem

imidazoles

Description

Usage

Format

Source

References

lm_design

Description

Optimal design problem

lme_design

Description

Optimal design problem

nlm_design

Description

Optimal design problem

scs_control_dflt

Description

Usage

Arguments

Seealso