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Mixed-Integer Linear Programming (MILP) Algorithms

Mixed-Integer Linear Programming Definition

A mixed-integer linear program (MILP) is a problem with

• Linear objective function, fTx, where f is a column vector of constants, and x is the column vector of unknowns

• Bounds and linear constraints, but no nonlinear constraints (for definitions, see Write Constraints)

• Restrictions on some components of x to have integer values

In mathematical terms, given vectors f, lb, and ub, matrices A and Aeq, corresponding vectors b and beq, and a set of indices `intcon`, find a vector x to solve

Legacy `intlinprog` Algorithm

Algorithm Overview

The `'legacy'` `intlinprog` algorithm uses this basic strategy to solve mixed-integer linear programs. `intlinprog` can solve the problem in any of the stages. If it solves the problem in a stage, `intlinprog` does not execute the later stages.

1. Reduce the problem size using Linear Program Preprocessing.

2. Solve an initial relaxed (noninteger) problem using Linear Programming.

3. Perform Mixed-Integer Program Preprocessing to tighten the LP relaxation of the mixed-integer problem.

4. Try Cut Generation to further tighten the LP relaxation of the mixed-integer problem.

5. Try to find integer-feasible solutions using heuristics.

6. Use a Branch and Bound algorithm to search systematically for the optimal solution. This algorithm solves LP relaxations with restricted ranges of possible values of the integer variables. It attempts to generate a sequence of updated bounds on the optimal objective function value.

Linear Program Preprocessing

According to the Mixed-Integer Linear Programming Definition, there are matrices A and Aeq and corresponding vectors b and beq that encode a set of linear inequalities and linear equalities

`$\begin{array}{c}A\text{ }·\text{ }x\le b\\ Aeq\text{ }·\text{ }x=beq.\end{array}$`

These linear constraints restrict the solution x.

Usually, it is possible to reduce the number of variables in the problem (the number of components of x), and reduce the number of linear constraints. While performing these reductions can take time for the solver, they usually lower the overall time to solution, and can make larger problems solvable. The algorithms can make solution more numerically stable. Furthermore, these algorithms can sometimes detect an infeasible problem.

Preprocessing steps aim to eliminate redundant variables and constraints, improve the scaling of the model and sparsity of the constraint matrix, strengthen the bounds on variables, and detect the primal and dual infeasibility of the model.

For details, see Andersen and Andersen [3] and Mészáros and Suhl [10].

Linear Programming

The initial relaxed problem is the linear programming problem with the same objective and constraints as Mixed-Integer Linear Programming Definition, but no integer constraints. Call xLP the solution to the relaxed problem, and x the solution to the original problem with integer constraints. Clearly,

fTxLPfTx,

because xLP minimizes the same function but with fewer restrictions.

This initial relaxed LP (root node LP) and all generated LP relaxations during the branch-and-bound algorithm are solved using linear programming solution techniques.

Mixed-Integer Program Preprocessing

During mixed-integer program preprocessing, `intlinprog` analyzes the linear inequalities `A*x ≤ b` along with integrality restrictions to determine whether:

• The problem is infeasible.

• Some bounds can be tightened, and therefore some variables can be fixed.

• Some inequalities are redundant, so can be ignored or removed.

• Some inequalities can be strengthened.

The `IntegerPreprocess` option lets you choose whether `intlinprog` takes several steps, takes all of them, or takes almost none of them. If you include an `x0` argument, `intlinprog` uses that value in preprocessing.

The main goal of mixed-integer program preprocessing is to simplify ensuing branch-and-bound calculations. Preprocessing involves quickly preexamining and eliminating some of the futile subproblem candidates that branch-and-bound would otherwise analyze.

For details about integer preprocessing, see Achterberg et al. [1].

Cut Generation

Cuts are additional linear inequality constraints that `intlinprog` adds to the problem. These inequalities attempt to restrict the feasible region of the LP relaxations so that their solutions are closer to integers. You control the type of cuts that `intlinprog` uses with the `CutGeneration` option.

`'basic'` cuts include:

• Mixed-integer rounding cuts

• Gomory cuts

• Clique cuts

• Cover cuts

• Flow cover cuts

Furthermore, if the problem is purely integer (all variables are integer-valued), then `intlinprog` also uses the following cuts:

• Strong Chvatal-Gomory cuts

• Zero-half cuts

`'intermediate'` cuts include all `'basic'` cuts, plus:

• Simple lift-and-project cuts

• Simple pivot-and-reduce cuts

• Reduce-and-split cuts

`'advanced'` cuts include all `'intermediate'` cuts except reduce-and-split cuts, plus:

• Strong Chvatal-Gomory cuts

• Zero-half cuts

For purely integer problems, `'intermediate'` uses the most cut types, because it uses reduce-and-split cuts, while `'advanced'` does not.

Another option, `CutMaxIterations`, specifies an upper bound on the number of times `intlinprog` iterates to generate cuts.

For details about cut generation algorithms (also called cutting plane methods), see Cornuéjols [6] and, for clique cuts, Atamtürk, Nemhauser, and Savelsbergh [4].

Heuristics for Finding Feasible Solutions

To get an upper bound on the objective function, the branch-and-bound procedure must find feasible points. A solution to an LP relaxation during branch-and-bound can be integer feasible, which can provide an improved upper bound to the original MILP. Certain techniques find feasible points faster before or during branch-and-bound. `intlinprog` uses these techniques at the root node and during some branch-and-bound iterations. These techniques are heuristic, meaning they are algorithms that can succeed but can also fail.

Heuristics can be start heuristics, which help the solver find an initial or new integer-feasible solution. Or heuristics can be improvement heuristics, which start at an integer-feasible point and attempt to find a better integer-feasible point, meaning one with lower objective function value. The `intlinprog` improvement heuristics are `'rins'`, `'rss'`, 1-opt, 2-opt, and guided diving.

Set the `intlinprog` heuristics using the `'Heuristics'` option. The options are:

OptionDescription
`'basic'` (default)

The solver runs rounding heuristics twice with different parameters, runs diving heuristics twice with different parameters, then runs `'rss'`. The solver does not run later heuristics when earlier heuristics lead to a sufficiently good integer-feasible solution.

`'intermediate'`

The solver runs rounding heuristics twice with different parameters, then runs diving heuristics twice with different parameters. If there is an integer-feasible solution, the solver then runs `'rins'` followed by `'rss'`. If `'rss'` finds a new solution, the solver runs `'rins'` again. The solver does not run later heuristics when earlier heuristics lead to a sufficiently good integer-feasible solution.

`'advanced'`

The solver runs rounding heuristics twice with different parameters, then runs diving heuristics twice with different parameters. If there is an integer-feasible solution, the solver then runs `'rins'` followed by `'rss'`. If `'rss'` finds a new solution, the solver runs `'rins'` again. The solver does not run later heuristics when earlier heuristics lead to a sufficiently good integer-feasible solution.

`'rins'` or the equivalent `'rins-diving'`

`intlinprog` searches the neighborhood of the current, best integer-feasible solution point (if available) to find a new and better solution. See Danna, Rothberg, and Le Pape [8]. When you select `'rins'`, the solver runs rounding heuristics twice with different parameters, runs diving heuristics twice with different parameters, then runs `'rins'`.

`'rss'` or the equivalent `'rss-diving'`

`intlinprog` applies a hybrid procedure combining ideas from `'rins'` and local branching to search for integer-feasible solutions. When you select `'rss'`, the solver runs rounding heuristics twice with different parameters, runs diving heuristics twice with different parameters, then runs `'rss'`. The solver does not run later heuristics when earlier heuristics lead to a sufficiently good integer-feasible solution. These settings perform the same heuristics as `'basic'`.

`'round'`

`intlinprog` takes the LP solution to the relaxed problem at a node, and rounds the integer components in a way that attempts to maintain feasibility. When you select `'round'`, the solver, at the root node, runs rounding heuristics twice with different parameters, then runs diving heuristics twice with different parameters. Thereafter, the solver runs only rounding heuristics at some branch-and-bound nodes.

`'round-diving'`

The solver works in a similar way to `'round'`, but also runs diving heuristics (in addition to rounding heuristics) at some branch-and-bound nodes, not just the root node.

`'diving'`

`intlinprog` uses heuristics that are similar to branch-and-bound steps, but follow just one branch of the tree down, without creating the other branches. This single branch leads to a fast “dive” down the tree fragment, thus the name “diving.” Currently, `intlinprog` uses six diving heuristics in this order:

• Vector length diving

• Coefficient diving

• Fractional diving

• Pseudo cost diving

• Line search diving

• Guided diving (applies when the solver already found at least one integer-feasible point)

Diving heuristics generally select one variable that should be integer-valued, for which the current solution is fractional. The heuristics then introduce a bound that forces the variable to be integer-valued, and solve the associated relaxed LP again. The method of choosing the variable to bound is the main difference between the diving heuristics. See Berthold [5], Section 3.1.

`'none'`

`intlinprog` does not search for a feasible point. The solver simply takes any feasible point it encounters in its branch-and-bound search.

The main difference between `'intermediate'` and `'advanced'` is that `'advanced'` runs heuristics more frequently during branch-and-bound iterations.

In addition to the previous table, the following heuristics run when the `Heuristics` option is `'basic'`, `'intermediate'`, or `'advanced'`.

• ZI round — This heuristic runs whenever an algorithm solves a relaxed LP. The heuristic goes through each fractional integer variable to attempt to shift it to a neighboring integer without affecting the feasibility with respect to other constraints. For details, see Hendel [9].

• 1-opt — This heuristic runs whenever an algorithm finds a new integer-feasible solution. The heuristic goes through each integer variable to attempt to shift it to a neighboring integer without affecting the feasibility with respect to other constraints, while lowering the objective function value.

• 2-opt — This heuristic runs whenever an algorithm finds a new integer-feasible solution. 2-opt finds all pairs of integer variables that affect the same constraint, meaning they have nonzero entries in the same row of an `A` or `Aeq` constraint matrix. For each pair, 2-opt takes an integer-feasible solution and moves the values of the variable pairs up or down using all four possible moves (up-up, up-down, down-up, and down-down), looking for a feasible neighboring solution that has a better objective function value. The algorithm tests each integer variable pair by calculating the largest size (same magnitude) of shifts for each variable in the pair that satisfies the constraints and also improves the objective function value.

At the beginning of the heuristics phase, `intlinprog` runs the trivial heuristic unless `Heuristics` is `'none'` or you provide an initial integer-feasible point in the `x0` argument. The trivial heuristic checks the following points for feasibility:

• All zeros

• Upper bound

• Lower bound (if nonzero)

• "Lock" point

The "lock" point is defined only for problems with finite upper and lower bounds for all variables. The "lock" point for each variable is its upper or lower bound, chosen as follows. For each variable `j`, count the number of corresponding positive entries in the linear constraint matrix `A(:,j)` and subtract the number corresponding negative entries. If the result is positive, use the lower bound for that variable, `lb(j)`. Otherwise, use the upper bound for that variable, `ub(j)`. The "lock" point attempts to satisfy the largest number of linear inequality constraints for each variable, but is not necessarily feasible.

After each heuristic completes with a feasible solution, `intlinprog` calls output functions and plot functions. See intlinprog Output Function and Plot Function Syntax.

If you include an `x0` argument, `intlinprog` uses that value in the `'rins'` and guided diving heuristics until it finds a better integer-feasible point. So when you provide `x0`, you can obtain good results by setting the `'Heuristics'` option to `'rins-diving'` or another setting that uses `'rins'`.

Branch and Bound

The branch-and-bound method constructs a sequence of subproblems that attempt to converge to a solution of the MILP. The subproblems give a sequence of upper and lower bounds on the solution fTx. The first upper bound is any feasible solution, and the first lower bound is the solution to the relaxed problem. For a discussion of the upper bound, see Heuristics for Finding Feasible Solutions.

As explained in Linear Programming, any solution to the linear programming relaxed problem has a lower objective function value than the solution to the MILP. Also, any feasible point xfeas satisfies

fTxfeasfTx,

because fTx is the minimum among all feasible points.

In this context, a node is an LP with the same objective function, bounds, and linear constraints as the original problem, but without integer constraints, and with particular changes to the linear constraints or bounds. The root node is the original problem with no integer constraints and no changes to the linear constraints or bounds, meaning the root node is the initial relaxed LP.

From the starting bounds, the branch-and-bound method constructs new subproblems by branching from the root node. The branching step is taken heuristically, according to one of several rules. Each rule is based on the idea of splitting a problem by restricting one variable to be less than or equal to an integer J, or greater than or equal to J+1. These two subproblems arise when an entry in xLP, corresponding to an integer specified in intcon, is not an integer. Here, xLP is the solution to a relaxed problem. Take J as the floor of the variable (rounded down), and J+1 as the ceiling (rounded up). The resulting two problems have solutions that are larger than or equal to fTxLP, because they have more restrictions. Therefore, this procedure potentially raises the lower bound.

The performance of the branch-and-bound method depends on the rule for choosing which variable to split (the branching rule). The algorithm uses these rules, which you can set in the `BranchRule` option:

• `'maxpscost'` — Choose the fractional variable with maximal pseudocost.

• `'strongpscost'` — Similar to `'maxpscost'`, but instead of the pseudocost being initialized to `1` for each variable, the solver attempts to branch on a variable only after the pseudocost has a more reliable estimate. To obtain a more reliable estimate, the solver does the following (see Achterberg, Koch, and Martin [2]).

• Order all potential branching variables (those that are currently fractional but should be integer) by their current pseudocost-based scores.

• Run the two relaxed linear programs based on the current branching variable, starting from the variable with the highest score (if the variable has not yet been used for a branching calculation). The solver uses these two solutions to update the pseudocosts for the current branching variable. The solver can halt this process early to save time in choosing the branch.

• Continue choosing variables in the list until the current highest pseudocost-based score does not change for `k` consecutive variables, where `k` is an internally chosen value, usually between 5 and 10.

• Branch on the variable with the highest pseudocost-based score. The solver might have already computed the relaxed linear programs based on this variable during an earlier pseudocost estimation procedure.

Because of the extra linear program solutions, each iteration of `'strongpscost'` branching takes longer than the default `'maxpscost'`. However, the number of branch-and-bound iterations typically decreases, so the `'strongpscost'` method can save time overall.

• `'reliability'` — Similar to `'strongpscost'`, but instead of running the relaxed linear programs only for uninitialized pseudocost branches, `'reliability'` runs the programs up to `k2` times for each variable, where `k2` is a small integer such as 4 or 8. Therefore, `'reliability'` has even slower branching, but potentially fewer branch-and-bound iterations, compared to `'strongpscost'`.

• `'mostfractional'` — Choose the variable with fractional part closest to `1/2`.

• `'maxfun'` — Choose the variable with maximal corresponding absolute value in the objective vector `f`.

After the algorithm branches, there are two new nodes to explore. The algorithm chooses which node to explore among all that are available using one of these rules:

• `'minobj'` — Choose the node that has the lowest objective function value.

• `'mininfeas'` — Choose the node with the minimal sum of integer infeasibilities. This means for every integer-infeasible component x(i) in the node, add up the smaller of pi and pi+, where

pi = x(i) – ⌊x(i)⌋
pi+ = 1 – pi.

• `'simplebestproj'` — Choose the node with the best projection.

`intlinprog` skips the analysis of some subproblems by considering information from the original problem such as the objective function’s greatest common divisor (GCD).

The branch-and-bound procedure continues, systematically generating subproblems to analyze and discarding the ones that won’t improve an upper or lower bound on the objective, until one of these stopping criteria is met:

• The algorithm exceeds the `MaxTime` option.

• The difference between the lower and upper bounds on the objective function is less than the `AbsoluteGapTolerance` or `RelativeGapTolerance` tolerances.

• The number of explored nodes exceeds the `MaxNodes` option.

• The number of integer feasible points exceeds the `MaxFeasiblePoints` option.

For details about the branch-and-bound procedure, see Nemhauser and Wolsey [11] and Wolsey [13].

HiGHS MILP Algorithm

Overview of HiGHS

The `intlinprog` `"highs"` algorithm is based on the HiGHS open-source software. `intlinprog` converts MATLAB®-formatted inputs and options into equivalent HiGHS arguments, and converts the returned solution into standard MATLAB format as well.

Algorithm Outline

The `"highs"` algorithm performs these steps.

1. Presolve

2. Evaluate Root Node

3. Get a branch/bound node (if none then stop)

4. Repeat until a stopping condition:

1. Search until a stopping condition:

2. Propagate domain

3. Prune nodes, update bounds, exit if infeasibility detected or `ObjectiveCutOff` option value is reached

4. If restart conditions are met, return to step 2

5. Install the next node:

1. Choose and evaluate a node

2. If evaluation prunes this node, return to step 5

3. Generate cuts for the node

4. If domain is infeasible, cut off the node, and bring open nodes into the node queue

5. Update the basis

6. Go to step 4

Presolve

Usually, it is possible to reduce the number of variables in the problem (the number of components of x), and reduce the number of linear constraints. While performing these reductions can take time for the solver, they usually lower the overall time to solution, and can make larger problems solvable. The algorithms can make solution more numerically stable. Furthermore, these algorithms can sometimes detect an infeasible problem.

Presolve steps aim to eliminate redundant variables and constraints, improve the scaling of the model and sparsity of the constraint matrix, strengthen the bounds on variables, and detect the primal and dual infeasibility of the model. For background, see Andersen and Andersen [3] and Mészáros and Suhl [10].

During mixed-integer program preprocessing, `intlinprog` analyzes the linear inequalities `A*x ≤ b` along with integrality restrictions to determine whether:

• The problem is infeasible.

• Some bounds can be tightened.

• Some inequalities are redundant, so can be ignored or removed.

• Some inequalities can be strengthened.

• Some integer variables can be fixed.

For background about integer preprocessing, see Achterberg et al. [1].

Evaluate Root Node

To evaluate the root node, the algorithm performs the following steps.

1. Detect symmetry and simplify problem.

2. Evaluate root LP.

3. Generate and add LP cuts (see Cut Generation).

4. Apply randomized rounding.

5. Generate and add more LP cuts.

6. Perform cut generation and heuristics in a loop.

7. Check for restart conditions, restart at step 2 if warranted.

After the root node evaluation completes, the algorithm proceeds with Branch-and-Bound.

Cut Generation

Cuts are additional linear inequality constraints that `intlinprog` adds to the problem. These inequalities attempt to restrict the feasible region of the LP relaxations so that their solutions are closer to integers. For background about cut generation algorithms (also called cutting plane methods), see Cornuéjols [6] and Atamtürk, Nemhauser, and Savelsbergh [4].

Plunge/Diving

To find integer-feasible points, `intlinprog` uses heuristics that are similar to branch-and-bound steps, but follow just one branch of the tree down, without creating the other branches. This single branch leads to a fast “dive” down the tree fragment, thus the name “diving.”

Diving heuristics generally select one variable that should be integer-valued, for which the current solution is fractional. The heuristics then introduce a bound that forces the variable to be integer-valued, and solve the associated relaxed LP again. The method of choosing the variable to bound is the main difference between the diving heuristics. See Berthold [5], Section 3.1.

Randomized Rounding, RINS, and RENS

To find new integer-feasible points, `intlinprog` searches the neighborhood of the current, best integer-feasible solution point (if available) to find a new and better solution. See Danna, Rothberg, and Le Pape [8]. Similarly, to find new integer-feasible points, `intlinprog` takes the LP solution to the relaxed problem at a node, and rounds the integer components in a way that attempts to maintain feasibility. By taking randomized rounding steps, `intlinprog` can sometimes find a new feasible point. RENS, which stands for Relaxation Enforced Neighborhood Search, is another search technique for finding integer-feasible points. See Berthold [5].

Branch-and-Bound

The branch-and-bound method constructs a sequence of subproblems that attempt to converge to a solution of the MILP. The subproblems give a sequence of upper and lower bounds on the solution fTx. These bounds are called the primal and dual bounds. For a minimization problem, the first upper bound (primal) is any feasible solution, and the first lower bound (dual) is the solution to the relaxed problem. For a maximization problem, the primal bound is the lower bound and the dual bound is the upper bound. As explained in Linear Programming, for a minimization problem, any solution to the linear programming relaxed problem has a lower objective function value than the solution to the MILP. Also, any feasible point xfeas satisfies

 fTxfeas ≥ fTx, (1)

because fTx is the minimum among all feasible points.

In this context, a node is an LP with the same objective function, bounds, and linear constraints as the original problem, but without integer constraints, and with particular changes to the linear constraints or bounds. The root node is the original problem with no integer constraints and no changes to the linear constraints or bounds, meaning the root node is the initial relaxed LP.

From the starting bounds, the branch-and-bound method constructs new subproblems by branching from the root node. The branching step is taken heuristically, according to one of several rules. Each rule is based on the idea of splitting a problem by restricting one variable to be less than or equal to an integer J, or greater than or equal to J+1. These two subproblems arise when an entry in xLP, corresponding to an integer specified in `intcon`, is not an integer. Here, xLP is the solution to a relaxed problem. Take J as the floor of the variable (rounded down), and J+1 as the ceiling (rounded up). The resulting two problems have solutions that are larger than or equal to fTxLP, because they have more restrictions. Therefore, for a minimization problem this procedure potentially raises the lower bound.

After the algorithm branches, there are two new nodes to explore. `intlinprog` skips the analysis of some subproblems by comparing their objective function values with the current global bounds.

The branch-and-bound procedure continues, systematically generating subproblems to analyze and discarding the ones that will not improve an upper or lower bound on the objective, until one of these stopping criteria is met:

• The problem is proved to be infeasible.

• The objective function value reaches the `ObjectiveCutOff` limit.

• The algorithm exceeds the `MaxTime` option.

• The difference between the lower and upper bounds on the objective function is less than the `AbsoluteGapTolerance` or `RelativeGapTolerance` tolerances.

• The number of explored nodes exceeds the `MaxNodes` option.

For background about the branch-and-bound procedure, see Nemhauser and Wolsey [11] and Wolsey [13].

Iterative Display

When you select iterative display by setting the `Display` option to the default `"iter"`, the solver displays some of its steps. HiGHS iterative display is more extensive and complicated than the iterative display of other solvers. Furthermore, the HiGHS algorithm can restart its branch-and-bound search, leading to an iterative display that also restarts.

To select iterative display:

```options = optimoptions("intlinprog",Display="iter"); [x,fval,exitflag,output] = intlinprog(f,intcon,A,b,Aeq,beq,... lb,ub,options)```

Preamble.  The iterative display begins by displaying the results of "presolve." The presolve algorithm reduces the complexity of the original problem by identifying and removing redundant rows and columns in the linear constraint matrices and performing related simplifications of the problem. For example,

```Presolving model 18018 rows, 26027 cols, 248579 nonzeros 15092 rows, 24343 cols, 217277 nonzeros Objective function is integral with scale 1```

Root Node Evaluation.  The root node is the linear programming solution of the problem, not considering any integer constraints. For a minimization problem, the objective function value of the root node is a lower bound on the objective function value of the solution to the problem including integer constraints. For a minimization problem, the upper bound (if any) comes from a feasible point with respect to all constraints. If there is no feasible point yet found, the upper bound is `Inf`.

The iterative display shows the size of the problem after presolve.

```Solving MIP model with: 15092 rows 24343 cols (24343 binary, 0 integer, 0 implied int., 0 continuous) 217277 nonzeros```
• `binary` is the number of binary variables.

• `integer` is the number of integer variables.

• `implied int` is the number of variables that are implied to be integer. For example, if `x(1)` is integer and `x(1) + x(2) = 5`, then `x(2)` is implied to be integer.

• `continuous` is the number of continuous variables.

The total number of variables is the number of columns in the model.

Dynamic Constraint Creation.  The software starts by creating "dynamic constraints," which have three headers in the iterative display:

• Cuts — Number of active cuts

• inLp — Number of non-model rows in the LP matrix

• Confl. — Number of conflicts

The software can further extend the constraints by restarting the dynamic constraint creation, which can create more constraints by starting the creation process from a new state.

In the last step before beginning the branch-and-bound process, the software reports the results of symmetry detection and the number of generators and orbitopes found. For example, this is the portion of the iterative display that appears in the preamble and dynamic constraint creation phase:

``` Nodes | B&B Tree | Objective Bounds | Dynamic Constraints | Work Proc. InQueue | Leaves Expl. | BestBound BestSol Gap | Cuts InLp Confl. | LpIters Time 0 0 0 0.00% 0 inf inf 0 0 0 0 1.5s 0 0 0 0.00% 0 inf inf 0 0 5 4934 3.6s … 0 0 0 0.00% 0 inf inf 4630 553 289 140296 159.6s 0.0% inactive integer columns, restarting Model after restart has 15090 rows, 24338 cols (24338 bin., 0 int., 0 impl., 0 cont.), and 217075 nonzeros 0 0 0 0.00% 0 inf inf 550 0 0 140296 160.5s … 0 0 0 0.00% 0 inf inf 5602 524 260 277323 318.0s 6.2% inactive integer columns, restarting Model after restart has 14185 rows, 22816 cols (22816 bin., 0 int., 0 impl., 0 cont.), and 200423 nonzeros 0 0 0 0.00% 0 inf inf 524 0 0 277323 318.6s … 0 0 0 0.00% 0 inf inf 4683 535 525 408393 505.8s Symmetry detection completed in 3.1s Found 215 generators and 12 full orbitope(s) acting on 730 columns```

For information about symmetry detection and orbitopes, see Hojny, Pfetsch, and Schmitt [7] and Pfetsch and Rehn [12]. The software continues to add dynamic constraints as it proceeds with branch-and-bound steps, described next.

Branch-and-Bound.  The branch-and-bound method constructs a sequence of subproblems that attempt to converge to a solution of the MILP. The subproblems give a sequence of upper and lower bounds on the solution fTx. For a minimization problem, the first upper bound is any feasible solution, and the first lower bound is the solution to the relaxed problem.

During the branch-and-bound procedure, `intlinprog` gives the following iterative display.

``` Nodes | B&B Tree | Objective Bounds | Dynamic Constraints | Work Proc. InQueue | Leaves Expl. | BestBound BestSol Gap | Cuts InLp Confl. | LpIters Time 72 0 2 1.56% 0 inf inf 4695 535 738 667009 686.9s … T 271 107 51 1.56% 0 449 100.00% 6051 271 1767 792786 776.8s T 279 107 53 1.56% 0 439 100.00% 6053 271 1794 793241 777.5s … L 1223 538 295 1.98% 0 434 100.00% 6984 243 7628 1689k 1580.6s … 1321 539 333 1.98% 0 434 100.00% 7029 243 8628 1898k 1650.6s Restarting search from the root node Model after restart has 13947 rows, 22426 cols (22426 bin., 0 int., 0 impl., 0 cont.), and 194633 nonzeros 1323 0 0 0.00% 0 434 100.00% 243 0 0 1902k 1653.5s 1323 0 0 0.00% 0 434 100.00% 243 76 10 1905k 1655.7s … 1694 173 52 0.00% 0 434 100.00% 9411 318 2220 3205k 2584.3s B 1710 167 55 0.00% 0 433 100.00% 9415 318 2263 3207k 2586.2s 1726 224 56 0.00% 0 433 100.00% 9999 378 2307 3237k 2608.7s```

The leftmost column shows a code indicating how the new feasible point was found for that row of the display:

• `L` — While solving a sub-MIP problem during primal heuristics

• `T` — During tree search, while evaluating a node

• `B` — During branching

• `H` — By heuristics

• `P` — During startup, before solving MIP

• `C` — By central rounding

• `R` — By randomized rounding

• `S` — While solving an LP

• `F` — By Feasibility Pump

• `U` — From an unbounded LP

Finish.  The reason that the algorithm stopped and a summary of results appear at the end of the iterative display.

```Solving report Status Time limit reached Primal bound 14 Dual bound 0 Gap 100% (tolerance: 0.01%) Solution status feasible 14 (objective) 0 (bound viol.) 1.7763568394e-15 (int. viol.) 0 (row viol.) Timing 7200.02 (total) 3.08 (presolve) 0.00 (postsolve) Nodes 5830 LP iterations 9963775 (total) 2657448 (strong br.) 324908 (separation) 2140011 (heuristics) Solver stopped prematurely. Integer feasible point found. Intlinprog stopped because it exceeded the time limit, options.MaxTime = 7200 . The intcon variables are integer within tolerance, options.ConstraintTolerance = 1e-06.```
• `Status` — Reason the iterations stopped

• `Primal bound` — Upper bound on objective function value for a minimization problem, lower bound for a maximization problem

• `Dual bound` — Lower bound on objective function value for a maximization problem, upper bound for a minimization problem

• `Gap` — Relative gap between primal and dual bounds

• `Solution status`

• Status of the solution

• Objective function value, labeled `(objective)`

• Maximum violation of the solution with respect to variable bounds, labeled `(bound viol.)`

• Maximum violation of the integer-type variables from integer values, labeled ```(int. viol.)```

• Maximum violation of the solution with respect to the linear constraints, labeled ```(row viol.)```

• `Timing` — Timing of various solution phases in seconds

• `Nodes` — Number of nodes explored

• `LP iterations` — Number of linear program iterations by phase and in total

References

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