qp_solver

This function provides a general interface to solve quadratic programming problems using different optimization methods.
It supports Genetic Algorithm (GA), Differential Evolution (DEoptim), and Sequential Least Squares Programming (SLSQP).

Provides functions to calculate the minimum and maximum possible
values of Cronbach's alpha when item-level missing data are present.
Cronbach's alpha (Cronbach, 1951 <doi:10.1007/BF02310555>) is one of the most widely used
measures of internal consistency in the social, behavioral, and medical sciences
(Bland & Altman, 1997 <doi:10.1136/bmj.314.7080.572>; Tavakol & Dennick, 2011
<doi:10.5116/ijme.4dfb.8dfd>). However, conventional implementations assume
complete data, and listwise deletion is often applied when missingness occurs,
which can lead to biased or overly optimistic reliability estimates (Enders, 2003
<doi:10.1037/1082-989X.8.3.322>). This package implements computational strategies
including enumeration, Monte Carlo sampling, and optimization algorithms
(e.g., Genetic Algorithm, Differential Evolution, Sequential Least Squares
Programming) to obtain sharp lower and upper bounds of Cronbach's alpha under
arbitrary missing data patterns. The approach is motivated by Manski's partial
identification framework and pessimistic bounding ideas from optimization literature.

Biying Zhou

missalpha

Find Range of Cronbach Alpha with a Dataset Including Missing
Data

Feng Ji

qp_solver function

<dl><dt>n</dt>
<dd>An integer representing the number of decision variables.</dd>
<dt>A</dt>
<dd>A matrix representing the quadratic coefficients.</dd>
<dt>b</dt>
<dd>A numeric vector representing the linear coefficients.</dd>
<dt>c</dt>
<dd>A numeric scalar representing the constant term in the objective function.</dd>
<dt>x_max</dt>
<dd>An integer representing the upper bound for the decision variables. Default is 1.</dd>
<dt>method</dt>
<dd>A character string specifying the optimization method to use. Can be <code>'GA'</code>, <code>'DEoptim'</code>, or <code>'nloptr'</code>.</dd>
<dt>print_message</dt>
<dd>A logical value indicating whether to print optimization details. Default is FALSE.</dd>
<dt>...</dt>
<dd>Additional control parameters passed to the chosen optimization method.</dd></dl>

Arguments

General Solver for Quadratic Programming Problems — qp_solver

<dl>

<dt>n</dt>
<dd>An integer representing the number of decision variables.</dd>


<dt>A</dt>
<dd>A matrix representing the quadratic coefficients.</dd>


<dt>b</dt>
<dd>A numeric vector representing the linear coefficients.</dd>


<dt>c</dt>
<dd>A numeric scalar representing the constant term in the objective function.</dd>


<dt>x_max</dt>
<dd>An integer representing the upper bound for the decision variables. Default is 1.</dd>


<dt>method</dt>
<dd>A character string specifying the optimization method to use. Can be <code>'GA'</code>, <code>'DEoptim'</code>, or <code>'nloptr'</code>.</dd>


<dt>print_message</dt>
<dd>A logical value indicating whether to print optimization details. Default is FALSE.</dd>


<dt>...</dt>
<dd>Additional control parameters passed to the chosen optimization method.</dd>

</dl>

qp_solver: General Solver for Quadratic Programming Problems

Description

Usage

Value

Arguments

See Also