## Linear Programming Algorithms

### Linear Programming Definition

Linear programming is the problem of finding a vector x that minimizes a linear function fTx subject to linear constraints:

`$\underset{x}{\mathrm{min}}{f}^{T}x$`

such that one or more of the following hold:

A·x ≤ b
Aeq·x = beq
l ≤ x ≤ u.

### Interior-Point `linprog` Algorithm

The `linprog` `'interior-point'` algorithm is very similar to the interior-point-convex quadprog Algorithm. It also shares many features with the `linprog` `'interior-point-legacy'` algorithm. These algorithms have the same general outline:

1. Presolve, meaning simplification and conversion of the problem to a standard form.

2. Generate an initial point. The choice of an initial point is especially important for solving interior-point algorithms efficiently, and this step can be time-consuming.

3. Predictor-corrector iterations to solve the KKT equations. This step is generally the most time-consuming.

#### Presolve

The algorithm first tries to simplify the problem by removing redundancies and simplifying constraints. The tasks performed during the presolve step can include the following:

• Check if any variables have equal upper and lower bounds. If so, check for feasibility, and then fix and remove the variables.

• Check if any linear inequality constraint involves only one variable. If so, check for feasibility, and then change the linear constraint to a bound.

• Check if any linear equality constraint involves only one variable. If so, check for feasibility, and then fix and remove the variable.

• Check if any linear constraint matrix has zero rows. If so, check for feasibility, and then delete the rows.

• Determine if the bounds and linear constraints are consistent.

• Check if any variables appear only as linear terms in the objective function and do not appear in any linear constraint. If so, check for feasibility and boundedness, and then fix the variables at their appropriate bounds.

• Change any linear inequality constraints to linear equality constraints by adding slack variables.

If the algorithm detects an infeasible or unbounded problem, it halts and issues an appropriate exit message.

The algorithm might arrive at a single feasible point, which represents the solution.

If the algorithm does not detect an infeasible or unbounded problem in the presolve step, and if the presolve has not produced the solution, the algorithm continues to its next steps. After reaching a stopping criterion, the algorithm reconstructs the original problem, undoing any presolve transformations. This final step is the postsolve step.

For simplicity, if the problem is not solved in the presolve step, the algorithm shifts all finite lower bounds to zero.

#### Generate Initial Point

To set the initial point, `x0`, the algorithm does the following.

1. Initialize `x0` to `ones(n,1)`, where `n` is the number of elements of the objective function vector f.

2. Convert all bounded components to have a lower bound of 0. If component `i` has a finite upper bound `u(i)`, then `x0(i) = u/2`.

3. For components that have only one bound, modify the component if necessary to lie strictly inside the bound.

4. To put `x0` close to the central path, take one predictor-corrector step, and then modify the resulting position and slack variables to lie well within any bounds. For details of the central path, see Nocedal and Wright , page 397.

#### Predictor-Corrector

Similar to the `fmincon` interior-point algorithm, the `interior-point` algorithm tries to find a point where the Karush-Kuhn-Tucker (KKT) conditions hold. To describe these equations for the linear programming problem, consider the standard form of the linear programming problem after preprocessing:

• Assume for now that all variables have at least one finite bound. By shifting and negating components, if necessary, this assumption means that all x components have a lower bound of 0.

• $\overline{A}$ is the extended linear matrix that includes both linear inequalities and linear equalities. $\overline{b}$ is the corresponding linear equality vector. $\overline{A}$ also includes terms for extending the vector x with slack variables s that turn inequality constraints to equality constraints:

`$\overline{A}x=\left(\begin{array}{cc}{A}_{eq}& 0\\ A& I\end{array}\right)\left(\begin{array}{c}{x}_{0}\\ s\end{array}\right),$`

where x0 means the original x vector.

• t is the vector of slacks that convert upper bounds to equalities.

The Lagrangian for this system involves the following vectors:

• y, Lagrange multipliers associated with the linear equalities

• v, Lagrange multipliers associated with the lower bound (positivity constraint)

• w, Lagrange multipliers associated with the upper bound

The Lagrangian is

`$L={f}^{T}x-{y}^{T}\left(\overline{A}x-\overline{b}\right)-{v}^{T}x-{w}^{T}\left(u-x-t\right).$`

Therefore, the KKT conditions for this system are

`$\begin{array}{c}f-{\overline{A}}^{T}y-v+w=0\\ \overline{A}x=\overline{b}\\ x+t=u\\ {v}_{i}{x}_{i}=0\\ {w}_{i}{t}_{i}=0\\ \left(x,v,w,t\right)\ge 0.\end{array}$`

The `linprog` algorithm uses a different sign convention for the returned Lagrange multipliers than this discussion gives. This discussion uses the same sign as most literature. See `lambda`.

The algorithm first predicts a step from the Newton-Raphson formula, and then computes a corrector step. The corrector attempts to reduce the residual in the nonlinear complementarity equations sizi = 0. The Newton-Raphson step is

 $\left(\begin{array}{ccccc}0& -{\overline{A}}^{T}& 0& -I& I\\ \overline{A}& 0& 0& 0& 0\\ -I& 0& -I& 0& 0\\ V& 0& 0& X& 0\\ 0& 0& W& 0& T\end{array}\right)\left(\begin{array}{c}\Delta x\\ \Delta y\\ \Delta t\\ \begin{array}{l}\Delta v\\ \Delta w\end{array}\end{array}\right)=-\left(\begin{array}{c}f-{\overline{A}}^{T}y-v+w\\ \overline{A}x-\overline{b}\\ u-x-t\\ \begin{array}{l}VX\\ WT\end{array}\end{array}\right)=-\left(\begin{array}{c}{r}_{d}\\ {r}_{p}\\ {r}_{ub}\\ \begin{array}{l}{r}_{vx}\\ {r}_{wt}\end{array}\end{array}\right),$ (1)

where X, V, W, and T are diagonal matrices corresponding to the vectors x, v, w, and t respectively. The residual vectors on the far right side of the equation are:

• rd, the dual residual

• rp, the primal residual

• rub, the upper bound residual

• rvx, the lower bound complementarity residual

• rwt, the upper bound complementarity residual

The iterative display reports these quantities:

To solve Equation 1, first convert it to the symmetric matrix form

 $\left(\begin{array}{cc}-D& {\overline{A}}^{T}\\ \overline{A}& 0\end{array}\right)\left(\begin{array}{c}\Delta x\\ \Delta y\end{array}\right)=-\left(\begin{array}{c}R\\ {r}_{p}\end{array}\right),$ (2)

where

`$\begin{array}{l}D={X}^{-1}V+{T}^{-1}W\\ R=-{r}_{d}-{X}^{-1}{r}_{vx}+{T}^{-1}{r}_{wt}+{T}^{-1}W{r}_{ub}.\end{array}$`

All the matrix inverses in the definitions of D and R are simple to compute because the matrices are diagonal.

To derive Equation 2 from Equation 1, notice that the second row of Equation 2 is the same as the second matrix row of Equation 1. The first row of Equation 2 comes from solving the last two rows of Equation 1 for Δv and Δw, and then solving for Δt.

Equation 2 is symmetric, but it is not positive definite because of the –D term. Therefore, you cannot solve it using a Cholesky factorization. A few more steps lead to a different equation that is positive definite, and hence can be solved efficiently by Cholesky factorization.

The second set of rows of Equation 2 is

`$\overline{A}\Delta x=-{r}_{p}$`

and the first set of rows is

`$-D\Delta x+{\overline{A}}^{T}\Delta y=-R.$`

Substituting

`$\Delta x={D}^{-1}{\overline{A}}^{T}\Delta y+{D}^{-1}R$`

gives

 $\overline{A}{D}^{-1}{\overline{A}}^{T}\Delta y=-\overline{A}{D}^{-1}R-{r}_{p}.$ (3)

Usually, the most efficient way to find the Newton step is to solve Equation 3 for Δy using Cholesky factorization. Cholesky factorization is possible because the matrix multiplying Δy is obviously symmetric and, in the absence of degeneracies, is positive definite. Afterward, to find the Newton step, back substitute to find Δx, Δt, Δv, and Δw. However, when $\overline{A}$ has a dense column, it can be more efficient to solve Equation 2 instead. The `linprog` interior-point algorithm chooses the solution algorithm based on the density of columns.

For more algorithm details, see Mehrotra .

After calculating the corrected Newton step, the algorithm performs more calculations to get both a longer current step, and to prepare for better subsequent steps. These multiple correction calculations can improve both performance and robustness. For details, see Gondzio .

The predictor-corrector algorithm is largely the same as the full `quadprog` `'interior-point-convex'` version, except for the quadratic terms. See Full Predictor-Corrector.

#### Stopping Conditions

The predictor-corrector algorithm iterates until it reaches a point that is feasible (satisfies the constraints to within tolerances) and where the relative step sizes are small. Specifically, define

`$\rho =\mathrm{max}\left(1,‖\overline{A}‖,‖f‖,‖\overline{b}‖\right).$`

The algorithm stops when all of these conditions are satisfied:

`$\begin{array}{c}{‖{r}_{p}‖}_{1}+{‖{r}_{ub}‖}_{1}\le \rho \text{TolCon}\\ {‖{r}_{d}‖}_{\infty }\le \rho \text{TolFun}\\ {r}_{c}\le \text{TolFun,}\end{array}$`

where

`${r}_{c}=\underset{i}{\mathrm{max}}\left(\mathrm{min}\left(|{x}_{i}{v}_{i}|,|{x}_{i}|,|{v}_{i}|\right),\mathrm{min}\left(|{t}_{i}{w}_{i}|,|{t}_{i}|,|{w}_{i}|\right)\right).$`

rc essentially measures the size of the complementarity residuals xv and tw, which are each vectors of zeros at a solution.

### Interior-Point-Legacy Linear Programming

#### Introduction

The interior-point-legacy method is based on LIPSOL (), which is a variant of Mehrotra's predictor-corrector algorithm (), a primal-dual interior-point method.

#### Main Algorithm

The algorithm begins by applying a series of preprocessing steps (see Preprocessing). After preprocessing, the problem has the form

 (4)

The upper bounds constraints are implicitly included in the constraint matrix A. With the addition of primal slack variables s, Equation 4 becomes

 (5)

which is referred to as the primal problem: x consists of the primal variables and s consists of the primal slack variables. The dual problem is

 (6)

where y and w consist of the dual variables and z consists of the dual slacks. The optimality conditions for this linear program, i.e., the primal Equation 5 and the dual Equation 6, are

 (7)

where xizi and siwi denote component-wise multiplication.

The `linprog` algorithm uses a different sign convention for the returned Lagrange multipliers than this discussion gives. This discussion uses the same sign as most literature. See `lambda`.

The quadratic equations xizi = 0 and siwi = 0 are called the complementarity conditions for the linear program; the other (linear) equations are called the feasibility conditions. The quantity

xTz + sTw

is the duality gap, which measures the residual of the complementarity portion of F when (x,z,s,w) ≥ 0.

The algorithm is a primal-dual algorithm, meaning that both the primal and the dual programs are solved simultaneously. It can be considered a Newton-like method, applied to the linear-quadratic system F(x,y,z,s,w) = 0 in Equation 7, while at the same time keeping the iterates x, z, w, and s positive, thus the name interior-point method. (The iterates are in the strictly interior region represented by the inequality constraints in Equation 5.)

The algorithm is a variant of the predictor-corrector algorithm proposed by Mehrotra. Consider an iterate v = [x;y;z;s;w], where [x;z;s;w] > 0 First compute the so-called prediction direction

`$\Delta {v}_{p}=-{\left({F}^{T}\left(v\right)\right)}^{-1}F\left(v\right),$`

which is the Newton direction; then the so-called corrector direction

`$\Delta {v}_{c}=-{\left({F}^{T}\left(v\right)\right)}^{-1}F\left(v+\Delta {v}_{p}\right)-\mu \stackrel{^}{e},$`

where μ > 0 is called the centering parameter and must be chosen carefully. $\stackrel{^}{e}$ is a zero-one vector with the ones corresponding to the quadratic equations in F(v), i.e., the perturbations are only applied to the complementarity conditions, which are all quadratic, but not to the feasibility conditions, which are all linear. The two directions are combined with a step length parameter α > 0 and update v to obtain the new iterate v+:

`${v}^{+}=v+\alpha \left(\Delta {v}_{p}+\Delta {v}_{c}\right),$`

where the step length parameter α is chosen so that

v+ = [x+;y+;z+;s+;w+]

satisfies

[x+;z+;s+;w+] > 0.

In solving for the preceding predictor/corrector directions, the algorithm computes a (sparse) direct factorization on a modification of the Cholesky factors of A·AT. If A has dense columns, it instead uses the Sherman-Morrison formula. If that solution is not adequate (the residual is too large), it performs an LDL factorization of an augmented system form of the step equations to find a solution. (See Example 4 — The Structure of D in the MATLAB® `ldl` function reference page.)

The algorithm then loops until the iterates converge. The main stopping criteria is a standard one:

`$\mathrm{max}\left(\frac{‖{r}_{b}‖}{\mathrm{max}\left(1,‖b‖\right)},\frac{‖{r}_{f}‖}{\mathrm{max}\left(1,‖f‖\right)},\frac{‖{r}_{u}‖}{\mathrm{max}\left(1,‖u‖\right)},\frac{|{f}^{T}x-{b}^{T}y+{u}^{T}w|}{\mathrm{max}\left(1,|{f}^{T}x|,|{b}^{T}y-{u}^{T}w|\right)}\right)\le tol,$`

where

`$\begin{array}{c}{r}_{b}=Ax-b\\ {r}_{f}={A}^{T}y-w+z-f\\ {r}_{u}=\left\{x\right\}+s-u\end{array}$`

are the primal residual, dual residual, and upper-bound feasibility respectively ({x} means those x with finite upper bounds), and

`${f}^{T}x-{b}^{T}y+{u}^{T}w$`

is the difference between the primal and dual objective values, and tol is some tolerance. The sum in the stopping criteria measures the total relative errors in the optimality conditions in Equation 7.

The measure of primal infeasibility is ||rb||, and the measure of dual infeasibility is ||rf||, where the norm is the Euclidean norm.

#### Preprocessing

The algorithm first tries to simplify the problem by removing redundancies and simplifying constraints. The tasks performed during the presolve step can include the following:

• Check if any variables have equal upper and lower bounds. If so, check for feasibility, and then fix and remove the variables.

• Check if any linear inequality constraint involves only one variable. If so, check for feasibility, and then change the linear constraint to a bound.

• Check if any linear equality constraint involves only one variable. If so, check for feasibility, and then fix and remove the variable.

• Check if any linear constraint matrix has zero rows. If so, check for feasibility, and then delete the rows.

• Determine if the bounds and linear constraints are consistent.

• Check if any variables appear only as linear terms in the objective function and do not appear in any linear constraint. If so, check for feasibility and boundedness, and then fix the variables at their appropriate bounds.

• Change any linear inequality constraints to linear equality constraints by adding slack variables.

If the algorithm detects an infeasible or unbounded problem, it halts and issues an appropriate exit message.

The algorithm might arrive at a single feasible point, which represents the solution.

If the algorithm does not detect an infeasible or unbounded problem in the presolve step, and if the presolve has not produced the solution, the algorithm continues to its next steps. After reaching a stopping criterion, the algorithm reconstructs the original problem, undoing any presolve transformations. This final step is the postsolve step.

For simplicity, the algorithm shifts all lower bounds to zero.

While these preprocessing steps can do much to speed up the iterative part of the algorithm, if the Lagrange multipliers are required, the preprocessing steps must be undone since the multipliers calculated during the algorithm are for the transformed problem, and not the original. Thus, if the multipliers are not requested, this transformation back is not computed, and might save some time computationally.

### Dual-Simplex Algorithm

At a high level, the `linprog` `'dual-simplex'` algorithm essentially performs a simplex algorithm on the dual problem.

The algorithm begins by preprocessing as described in Preprocessing. For details, see Andersen and Andersen  and Nocedal and Wright , Chapter 13. This preprocessing reduces the original linear programming problem to the form of Equation 4:

A and b are transformed versions of the original constraint matrices. This is the primal problem.

Primal feasibility can be defined in terms of the + function

The measure of primal infeasibility is

As explained in Equation 6, the dual problem is to find vectors y and w, and a slack variable vector z that solve

The `linprog` algorithm uses a different sign convention for the returned Lagrange multipliers than this discussion gives. This discussion uses the same sign as most literature. See `lambda`.

The measure of dual infeasibility is

It is well known (for example, see ) that any finite solution of the dual problem gives a solution to the primal problem, and any finite solution of the primal problem gives a solution of the dual problem. Furthermore, if either the primal or dual problem is unbounded, then the other problem is infeasible. And if either the primal or dual problem is infeasible, then the other problem is either infeasible or unbounded. Therefore, the two problems are equivalent in terms of obtaining a finite solution, if one exists. Because the primal and dual problems are mathematically equivalent, but the computational steps differ, it can be better to solve the primal problem by solving the dual problem.

To help alleviate degeneracy (see Nocedal and Wright , page 366), the dual simplex algorithm begins by perturbing the objective function.

Phase 1 of the dual simplex algorithm is to find a dual feasible point. The algorithm does this by solving an auxiliary linear programming problem.

During Phase 2, the solver repeatedly chooses an entering variable and a leaving variable. The algorithm chooses a leaving variable according to a technique suggested by Forrest and Goldfarb  called dual steepest-edge pricing. The algorithm chooses an entering variable using the variation of Harris’ ratio test suggested by Koberstein . To help alleviate degeneracy, the algorithm can introduce additional perturbations during Phase 2.

The solver iterates, attempting to maintain dual feasibility while reducing primal infeasibility, until the solution to the reduced, perturbed problem is both primal feasible and dual feasible. The algorithm unwinds the perturbations that it introduced. If the solution (to the perturbed problem) is dual infeasible for the unperturbed (original) problem, then the solver restores dual feasibility using primal simplex or Phase 1 algorithms. Finally, the solver unwinds the preprocessing steps to return the solution to the original problem.

#### Basic and Nonbasic Variables

This section defines the terms basis, nonbasis, and basic feasible solutions for a linear programming problem. The definition assumes that the problem is given in the following standard form:

(Note that A and b are not the matrix and vector defining the inequalities in the original problem.) Assume that A is an m-by-n matrix, of rank m < n, whose columns are {a1a2, ..., an}. Suppose that $\left\{{a}_{{i}_{1}},{a}_{{i}_{2}},...,{a}_{{i}_{m}}\right\}$ is a basis for the column space of A, with index set B = {i1, i2, ..., im}, and that N = {1, 2, ..., n}\B is the complement of B. The submatrix AB is called a basis and the complementary submatrix AN is called a nonbasis. The vector of basic variables is xB and the vector of nonbasic variables is xN. At each iteration in phase 2, the algorithm replaces one column of the current basis with a column of the nonbasis and updates the variables xB and xN accordingly.

If x is a solution to A·x = b and all the nonbasic variables in xN are equal to either their lower or upper bounds, x is called a basic solution. If, in addition, the basic variables in xB satisfy their lower and upper bounds, so that x is a feasible point, x is called a basic feasible solution.

 Andersen, E. D., and K. D. Andersen. Presolving in linear programming. Math. Programming 71, 1995, pp. 221–245.

 Applegate, D. L., R. E. Bixby, V. Chvátal and W. J. Cook, The Traveling Salesman Problem: A Computational Study, Princeton University Press, 2007.

 Forrest, J. J., and D. Goldfarb. Steepest-edge simplex algorithms for linear programming. Math. Programming 57, 1992, pp. 341–374.

 Gondzio, J. “Multiple centrality corrections in a primal dual method for linear programming.” Computational Optimization and Applications, Volume 6, Number 2, 1996, pp. 137–156. Available at `https://www.maths.ed.ac.uk/~gondzio/software/correctors.ps`.

 Koberstein, A. Progress in the dual simplex algorithm for solving large scale LP problems: techniques for a fast and stable implementation. Computational Optim. and Application 41, 2008, pp. 185–204.

 Mehrotra, S. “On the Implementation of a Primal-Dual Interior Point Method.” SIAM Journal on Optimization, Vol. 2, 1992, pp 575–601.

 Nocedal, J., and S. J. Wright. Numerical Optimization, Second Edition. Springer Series in Operations Research, Springer-Verlag, 2006.