Preprint

Article

An Efficient Closed-form Formula for Evaluating r-flip Moves in Quadratic Unconstrained Binary Optimization

Altmetrics

Downloads

Views

Comments

A peer-reviewed article of this preprint also exists.

This version is not peer-reviewed

Submitted:

15 November 2023

Posted:

17 November 2023

You are already at the latest version

Alerts

Abstract

The quadratic unconstrained binary optimization (QUBO) is a classic NP-hard problem with an enormous number of applications. Local search strategy (LSS) is one of the most fundamental algorithmic concepts that has been successfully applied to a wide range of hard combinatorial optimization problems. One LSS that has gained the attention of researchers is the r-flip (also known as r-Opt) strategy. Given a binary solution with n variables, the r-flip strategy ‘flips’ r binary variables to get a new solution if the changes improve the objective function. The main purpose of this paper is to develop several results for implementation of r-flip moves in QUBO, including a necessary and sufficient condition that when a 1-flip search reaches local optimality, the number of candidates for implementation of the r-flip moves can be reduced significantly. The results of the substantial computational experiments are reported to compare an r-flip strategy embedded algorithm and a multiple start tabu search algorithm on a set of benchmark instances and three very-large-scale QUBO instances. The r-flip strategy implemented within the algorithm makes the algorithm very efficient, very high-quality solutions within a short CPU time.

Keywords:

Subject: Business, Economics and Management - Business and Management

1. Introduction

The quadratic unconstrained binary optimization is a classic NP-hard problem with an enormous number of real has been used as a unifying approach to many combinatorial optimization problems [2,3]. Due to its practicality, as well as theoretical interest, over the years researchers have proposed many theoretical results as well as simple and sophisticated approaches as solution procedures [4,5,6,7,8,9,10,11]. However, due to the complexity and practicality of QUBO it is still necessary to provide results suitable for solving large-scale problems. In recent years, researchers have developed theoretical results to reduce algorithmic implementation difficulty of QUBO, [12,13,14,15,16,17]. Our result in this paper also helps to reduce size and difficulty of algorithmic implementation of these problems.

The quadratic unconstrained binary optimization (QUBO) can be formulated as,

M a x f (x) = \sum_{i = 1}^{n} q_{i} x_{i} + \frac{1}{2} \sum_{i = 1}^{n} \sum_{j \neq i}^{n} q_{i, j} x_{i} x_{j}, s . t . x_{i} \in \{0,1\}, i = 1, \dots, n

(1)

In (1),

\frac{1}{2} q_{i, j}

is the i,j-th entry of a given n by n symmetric matrix Q. QUBO is often referred to as the

x^{T} Q x

model [18]. Since

{x_{i}}^{2} = x_{i}

, and Q may be written as an upper triangular matrix by doubling each entry of the upper triangle part of the matrix and letting

q_{i, i} = q_{i}

, then we can write (1) as (2).

M a x f (x) = \sum_{i = 1}^{n} \sum_{j \geq i}^{n} q_{i, j} x_{i} x_{j} = x^{T} Q x, s . t . x_i \in {0,1}, i = 1, \dots, n

(2)

Local search strategy (LSS) is one of the most fundamental algorithmic concepts that has been successfully applied to a wide range of hard combinatorial optimization problems. The basic ingredient of almost all sophisticated heuristics is some variation of LSS. One LSS that has been used by many researchers as a stand-alone or as a basic component of more sophisticated algorithms is the r-flip (also known as r-Opt) strategy [12,19,20,21,22,23]. In Figure A6, we present a comprehensive review of r-flip strategies applied to QUBO. Let N={1,2,…,n}. Given a binary solution,

x = (x_{1}, \dots, x_{n})

x^{T} Q x

, the r-flip search chooses a subset,

S \subseteq N

, with

|S| \leq r

, and builds a new solution,

x^{'}

, where

x_{i}^{'} = 1 - x_{i}

for all

i \in S

. If

x^{'}

improves the objective function, it is called an improving move (or improving subset S). The r-flip search starts with a solution x, chooses an improving subset S, and flips all elements in S. The process continues until there is no subset S with

|S| \leq r

that improves the objective function. The result is called a locally optimal solution with respect to the r-flip move (or r-Opt).

Often in strategies where variable neighborhood searches, such as fan-and-filter (F&F) [24,25], variable neighborhood search (VNS) [26,27], and multi-exchange neighborhood search (MENS)[19,20,21,22,23] are used, the value of r dynamically changes as the search progresses. Generally, there are two reasons for a dynamically changing search space strategy.

a): The execution of an implementation of an r-flip local search, for larger value of r, can be computationally expensive to execute. This is because the size of the search space is of order n chosen r, and for fixed values of n, it grows quickly in r for value of $r \leq ⌊n / 2⌋$ . Hence, smaller values of r, especially r equal to 1 and 2, have shown considerable success.
b): In practice, a r-flip local search process with a small value of r (e.g., r=1) can quickly reach local optimality. Thus, as a way to escape 1-flip local optimality, researchers have tried to dynamically change the value of r as the search progressed. This gives an opportunity to expand the search to a more diverse solution space.

A clever implementation of (a) and (b) in an algorithm can not only save computational time, since the smaller value of r is less computationally expensive, but it can also possibly reach better solutions because the larger values of r provide an opportunity to search more a diverse part of the solution space.

1.1. Previous Works

The development of closed form formulas for r-flip moves is desirable for developing heuristics for solving very-large-scale problem instances because it can reduce computational time consumed by an implementation of an algorithm. Alidaee, Kochenberger [12] introduced several theorems showing closed form r-flip formulas for general Pseudo-Boolean Optimization. Authors in [13,14] recently provided closed form formulas for evaluating r-flip rules in QUBO. In particular, Theorem 6 in [12] is specific to the

{f (x) = x}^{T} Q x

problem. To explain the closed form formula for the r-flip rule in

x^{T} Q x

, we first introduce a few definitions. Refer to Figure A6 for an exhaustive literature of r-flip rules applied to QUBO.

Given a solution

x = (x_{1}, \dots, x_{n})

, the derivative of f(x) with respect to

x_{i}

is defined as:

E (x_{i}) = q_{i} + \sum_{j < i} q_{j, i} x_{j} + \sum_{j > i} q_{i, j} x_{j}, i = 1, \dots, n

(3)

Fact 1. Given a solution vector

x = (x_{1}, \dots, x_{i}, \dots, x_{n}),

and a solution

x^{'} = (x_{1}, \dots, {1 - x}_{i}, \dots, x_{n})

obtained by flipping the ith element of x, we have:

∆ f = f (x^{'}) - f (x) = (x_{i}^{'} - x_{i}) E (x_{i}) .

(4)

It is well known that any locally optimal solution to an instance of the QUBO problem with respect to a 1-flip search satisfies,

E i t h e r ((x_{i} = 0) i f f E (x_{i}) \leq 0) o r (x_{i} = 1 i f f E (x_{i}) \geq 0), f o r i = 1, \dots, n

(5)

Furthermore, after changing x to x’, the update for

E (x_{j})

, j=1,…,n, can be calculated as follows:

\forall j < i, E (x_{j}) \leftarrow E (x_{j}) + q_{j, i} (x_{i}^{'} - x_{i})

\forall j > i, E (x_{j}) \leftarrow E (x_{j}) + q_{i, j} (x_{i}^{'} - x_{i})

(6)

j = i, E (x_{j}) \leftarrow E (x_{j})

Note that

x_{i}^{'} - x_{i}

may be written as

1 - 2 x_{i}

, which can simplify the implementation process. A simple 1-flip search is provided in Figure 1. Note that in line 3 we chose a sequence to implement Fact 1. Using such a strategy has experimentally proven to be very effective in several recent studies [28].

Before we present the algorithms in this study for the r-flip strategy, the notations used are given as follows:

n The number of variables

x A starting feasible solution

x* The best solution found so far by the algorithm

K The largest value of k for r-flip, k

\leq

π(i) The i-th element of x in the order π(1)⋯π(n)

S ={i:x_i is tentatively chosen to receive a new value to produce a new solution x_i'} restricting consideration to |S| = r

D The set of candidates for an improving move

Tabu_ten The maximum number of iterations for which a variable can remain Tabu

Tabu(i) A vector representing Tabu status of x

E (x_{i})

Derivative of f(x) with respect to

x_{i}

E (x) = (E (x_{1}), \dots, E (x_{n}))

The vector of derivatives

x(.) A vector representing the solution of x

E(.) A vector representing the value of derivative

E (x_{i})

The result of Fact 1 has been extended to the r-flip search, given below.

(Theorem 6, Alidaee, Kochenberger [12]) Let x be a given solution of QUBO and x’ obtained from x by r-flip move (for a chosen set S) where

S \subseteq N

, |S|=r, the change in the value of the objective function is:

∆ f = f (x') - f (x) = \sum_{i \in S} (x'_{i} - x_{i}) E (x_{i}) + \sum_{i, j \in S, i < j} (x'_{i} - x_{i}) (x'_{j} - x_{j}) q_{i, j}

(7)

Furthermore, after changing x to x’ the update for

E (x_{j})

,j=1,…,n, can be calculated as follows:

\forall j \in N \ S, E (x_{j}) \leftarrow E (x_{j}) + \sum_{i \in S} (x_{i}^{'} - x_{i}) q_{i, j}

\forall j \in S, E (x_{j}) \leftarrow E (x_{j}) + \sum_{i \in S \ {j}} (x_{i}^{'} - x_{i}) q_{i, j}

(8)

As explained in [12], the evaluation of change in the objective function (7) can be done in

O (r^{2})

, i.e., evaluating f(x’) from f(x). The update in (8) requires r calculations for each j in N\S, and r-1 calculations for each j in S, Thus, overall, update for all n variables can be performed in O(nr).

Note that for any two elements i,j=1,…,n, and i<j, we can define:

E' (x_{i}) = E (x_{i}) - q_{i} - q_{i, j} x_{j},

E' (x_{j}) = E (x_{j}) - q_{j} - q_{i, j} x_{i} .

(9)

Using (9), a useful way to express Equation (7) is Equation (10).

∆ f = \sum_{i \in S} [(1 - 2 x_{i}) E^{'} (x_{i}) + \sum_{j \in S, j \leq i} (1 - x_{i} - x_{j}) q_{i, j}]

(10)

A simple exhaustive r-flip search is provided in Figure 2. The complexity of the problem indicates that the use of a larger value of r in the r-flip local search can make the implementation of the search process more time consuming. Meanwhile, the larger value of r can provide an opportunity to search a more diverse area of search space and thus possibly reach better solutions. To overcome such conflicts, researchers often use r=1 (and occasionally r=2) as the basic components of their more complex algorithms, such as F&F, VNS, and MENS. Below, in Theorem 1 and Proposition 1, we prove that after reaching the locally optimal solution with respect to a 1-flip search, the implementation of an r-flip search can significantly be reduced. Further, related results are also provided to allow the efficient implementation of an r-flip search within an algorithm.

2. New Results on Closed-form Formulas

We first introduce some notations. For m<n, define (m, n) to be the number of combinations of m elements out of n, and let

φ = \underset{i, j \in N}{Max} \{|q_{i, j}|\}

, and

M = φ * (2, r)

. Furthermore, Lemma 1 and Lemma 2, presented below, help to prove the results. Note that, Lemma 1 is direct deduction from previous results [12].

Lemma 1. Given a locally optimal solution

x = (x_{1}, \dots, x_{n})

with respect to a 1-flip search, we have:

(x'_{i} - x_{i}) E (x_{i}) \leq 0, for i = 1, \dots, n .

(11)

Proof. Condition of local optimality in (5) indicates that:

(E (x_{i}) \geq 0 iff x_{i} = 1), and (E (x_{i}) \leq 0, iff x_{i} = 0) .

Using this condition, we thus have:

(x'_{i} - x_{i}) E (x_{i}) \leq 0, for i = 1, \dots, n .

Lemma 2. Let

x = (x_{1}, \dots, x_{n})

be any solution of the problem; then, we have:

\sum_{i, j \in S} (x_{i}^{'} - x_{i}) (x_{j}^{'} - x_{j}) q_{i, j} \leq M

(12)

Proof. For each pair of elements,

i, j \in S

, the left-hand-side can be

q_{i, j}

{- q}_{i, j}

. Since |S|=r, the summation on the left-hand-side is at most equal to M.

Theorem 1: Let

φ

and M be as defined above and let

x = (x_{1}, \dots, x_{n})

be a locally optimal solution of

x^{T} Q x

with respect to a 1-flip search. A subset

S \subseteq N

, with |S|=r, is an improving r-flip move if and only if we have:

\sum_{i \in S} |E (x_{i})| \leq \sum_{i, j \in S} (x_{i}^{'} - x_{i}) (x_{j}^{'} - x_{j}) q_{i, j}

(13)

Proof: Using (7), a subset

S \subseteq N

of r elements is an improving r-flip move if and only if we have:

∆ f = f (x^{'}) - f (x) = \sum_{i \in S} (x_{i}^{'} - x_{i}) E (x_{i}) + \sum_{i, j \in S} (x_{i}^{'} - x_{i}) (x_{j}^{'} - x_{j}) q_{i, j} > 0

(14)

Since x is a locally optimal solution with respect to a 1-flip search, it follows from Lemma 1 that inequality (14) is equivalent to (15); that completes the proof.

\sum_{i, j \in S} (x_{i}^{'} - x_{i}) (x_{j}^{'} - x_{j}) q_{i, j} > - \sum_{i \in S} (x_{i}^{'} - x_{i}) E (x_{i}) = \sum_{i \in S} |E (x_{i})|

(15)

Proposition 1: Let

φ

and M be as defined above and let

x = (x_{1}, \dots, x_{n})

be any locally optimal solution of the

x^{T} Q x

problem with respect to a 1-flip search. If a subset

S \subseteq N

, with |S|=r, is an improving r-flip move, then we must have:

\sum_{i \in S} |E (x_{i})| < M

Proof: Since x is a locally optimal solution with respect to a 1-flip search and S is an improving r-flip move, by Theorem 1, we have:

\sum_{i \in S} |E (x_{i})| < \sum_{i, j \in S} (x_{i}^{'} - x_{i}) (x_{j}^{'} - x_{j}) q_{i, j}

(15)

Using Lemma 2; we also have (16); which completes the proof.

\sum_{i \in S} |E (x_{i})| < \sum_{i, j \in S} (x_{i}^{'} - x_{i}) (x_{j}^{'} - x_{j}) q_{i, j} \leq M

(16)

The consequence of Theorem 1 is as follows. Given a locally optimal solution x with respect to a 1-flip search, if there is no subset of S with |S|=r that satisfies (13), then x is also locally optimal solution with respect to an r-flip search. Furthermore, if there is no subset S of any size that (13) is satisfied, then x is also locally optimal solution with respect to an r-flip search for all

r \leq n

. Similar statements are also true regarding Proposition 1.

The result of Proposition1 is significant in the implementation of an r-flip search. It illustrates that, after having a 1-flip search implemented, if an r-flip search is next served as a locally optimal solution, only those elements with the sum of absolute value of derivatives less than M are eligible for consideration. Furthermore, when deciding about the elements of an r-flip search, we can easily check to see if any element

x_{i}

by itself or with a combination of other elements is eligible to be a member of an improving r-flip move S. Example 1 below illustrates this situation.

Example 1. Consider an

x^{T} Q x

problem with n variables. Let

x = (x_{1}, \dots, x_{n})

be a given locally optimal solution with respect to a 1-flip search. Consider S={i,j,k,l} for a possible 4-flip move. In order to have S for an improving move, all 15 inequalities, given below in (17), must be satisfied. Of course, if the last inequality in (17) is satisfied, all other inequalities are also satisfied. This means each subset of the S is also an improving move. This is important in any dynamic neighborhood search strategies k-flip moves for

k \leq r

in consideration.

\begin{array}{l} Here we have φ = \underset{i, j \in N}{M a x} {| q_{i j} |}, and M = 6 * φ : \\ | E (x_{a}) | < M, for a = i, j, k, l, \\ | E (x_{a}) | + | E (x_{b}) | < M, for (a \neq b), a, b = i, j, k, l, \\ | E (x_{a}) | + | E (x_{b}) | + | E (x_{c}) | < M, for (a \neq b \neq c), a, b, c = i, j, k, l, \\ | E (x_{a}) | + | E (x_{b}) | + | E (x_{c}) | + | E (x_{d}) | < M, for a = i, b = j, c = k, d = l \end{array}

(17)

Obviously, choosing the appropriate subset S to implement a move is critical. There are many ways to check for an improving subset S. Below, we explain two such strategies. In addition, a numerical example is given in the Appendix.

2.1. Strategy 1

We first define a set, D(n), of candidate for improving moves. Given a locally optimal solution x with respect to a 1-flip move, let the elements of x be ordered in ascending absolute value of derivatives, as given in (18).

| E (x_{π (1)}) | \leq \dots \leq | E (x_{π (n)}) |

(18)

Here,

π (i)

means the i-th element of x in the order

(π (1), \dots, π (n))

. Let K be the largest value of k=1,2,…,n where the inequality (19) is satisfied. The set D(n) is now defined by (20).

\sum_{i = 1}^{k} | E (x_{π (i)}) | < M, for k = 1,2, 3, \dots, k

(19)

D (n) = {x_{π (1)}, \dots, x_{π (K)}}

(20)

Lemma 3. Any subset

S \subseteq D (n)

satisfies the necessary condition for an improving move.

Proof. It follows from Proposition 1.

There are some advantages to having elements of x in an ascending order, i.e., inequalities (18):

the smaller the value of $| E (x_{i}) |$ is, the more likely that $x_{i}$ is involved in an improving k-flip move for k<=r (this might be due to the fact that, the right-hand-side value M in (19) for given r is constant. Thus, smaller values of $| E (x_{i}) |$ on the left-hand-side might help to satisfy the inequality easier.)
because the elements of D(n) are in an ascending order of absolute values of derivatives, a straightforward implementable series of alternatives to be considered for improving subsets, S, may be the elements of the set given in (21). Note that there are a lot more subsets of D(n) compared to the sets in (21) that are the candidates for consideration in possible k-flip moves. Here we only gave one possible efficient implementable strategy.

$S \in \{{π (1), π (2)}, {π (1), π (2), π (3)}, \dots, {π (1), \dots, π (K)}\}$

(21)

It is important to note that, if Proposition 1 is used in the process of implementing an algorithm, given a locally optimal solution x with respect to a 1-flip search, after an r-flip implementation for a subset |S|=r with r>1, the locally optimal solution with respect to a 1-flip search for the new solution, x’, can be destroyed. Thus, if an r-flip search needed to be continued, a 1-flip search might be necessary on solution x’ before a new r-flip move can continue. However, there are many practical situations where this problem may be avoided for many subsets, especially when the problem is very-large-scale, i.e., the value of n is large, and/or Q is sparse. Proposition 2 is a weaker condition of Proposition 1 that can help to overcome up to some point in the aforementioned problem.

In the proof of Theorem 1 and Proposition 1, we only used a condition of optimality for a 1-flip search satisfied for the members of the subset S. We now define a condition as follows and call it ‘condition of optimality with respect to a 1-flip search for a set S’, or simply ‘condition of optimality for S’.

Given a solution x, the condition of optimality for any subset

S \subseteq N

is satisfied if and only if we have:

E i t h e r (x_{i} = 0 i f f E (i) \leq 0) o r (x_{i} = 1 i f f E (i) \geq 0), for i \in S

(22)

Of course, if we have N in (22) instead of S, x is a locally optimal solution as was defined in Fact 1.

For m<n, let (m, n) be the number of combinations of m elements out of n elements, and

φ_{S} = \max_{i, j \in S} \{|q_{i, j}|\}

, and

M_{S} = φ_{S} * (2, r)

. With these definitions now we state Proposition 2.

Proposition 2 (weak necessary condition): Let

S \subseteq N

, |S|=r, and

φ_{S}

, and

M_{S}

as defined above. Given any solution

x = (x_{1}, \dots, x_{n})

x^{T} Q x

, and assume the condition of optimality is satisfied for a subset S. If S is an r-flip improving move, we must have

\sum_{i \in S} | E (x_{i}) | < M_{S}

Proof: Similar to proof of Proposition 1.

Notice that the values of

φ_{S}

and

M_{S}

in Proposition 2 depend on S; however, these values can be updated efficiently as the search progresses. As explained above, in situations where the problem is very-large-scale and/or Q is sparce, for many variables, the values of derivatives are ‘unaffected’ by the change of values of elements in S. This means a large set of variables still satisfies the condition of optimality, and thus the search can continue without applying a 1-flip search each time before finding a new set S for r-flip implementation.

2.2. Strategy 2

Another efficient and easily implementable strategy is when instead of (19), we only use an individual element to create a set of candidates for applying an r-flip search, set D(1) as defined below. Corollary 1 is a special case of Proposition 1 that suffices such a strategy.

D (1) = {x_{i} : | E (x_{i}) | < M}

(23)

Corollary 1: Let

φ

and M be as defined before, given a solution

x = (x_{1}, \dots, x_{n})

x^{T} Q x

, if the 1-flip local search cannot further improve the value of f(x), and

i \in S

with

S \subseteq N

where an r-flip move of elements of S improves f(x), then we must have

| E (x_{i}) | < M

To gain insight into the use of Corollary 1, we did some experimentation to find the size of the set D(1) for different sizes of instances. The steps of the experiment to find the size of D(1) are given below. Problems considered are taken from literature [27], and used by many researchers. We only used the larger-scale problems with 2500 to 6000 variables, a total of 38 instances.

Find_D(): Procedure for finding the size of the set D(1):

Step 1. Randomly initialize a solution to the problem. For each value of r calculate M. Apply the algorithm in Figure 1 and generate a locally optimal solution x with respect to a 1-flip search. However, in Step 5 of Figure 1 only consider those derivatives with $| E (x_{i}) | < M$
Step 2. Find the number of elements in the set D(1) for x.
Step 3. Repeat Step 1 and 2, 200 times for each problem, and find the average number of elements in the set D(1) for the same size problem, density, and r value.

The results of the experiment are shown in Table 1. From Table 1, in general we can say that as the density of matrix Q increases, the size of D(1) decreases for all problem sizes and values of r. This is, of course, due to the fact that the larger density of Q makes the derivative of each element in an x more related to other elements. As the size of a problem increases, the size of D(1) also increases.

An interesting observation in our experiment was that, in most cases, the size of D(1) for better locally optimal solutions were smaller than those with the worse locally optimal solutions. This indicates that as the search reaches closer to the globally optimal solutions, the time for an r-flip search decreases when we take advantage of Corollary 1.

2.3. Implementation Details

We first implement two strategies in Section 2.1 and Section 2.2 via Algorithm 3 for Strategy 1 (see Figure 3), and Algorithm 4 for Strategy 2 (see Figure 4), then propose Algorithm 5 for Strategy 2 embedded with a simple tabu search algorithm for the improvement in Figure 5.

In the Destruction() procedure, there are three steps:

step 3a. Find the variable that is not on the tabu list and lead to the small change to the solution when the variable is flipped.
step 3b. Change its value, place it on the tabu list to update the tabu list, update E(x).
step 3c. Test if there is any variable that is not on the tabu list and can improve the solution. If not, go to Step 3a.

In the Construction() procedure, there are four steps:

step 4a. Test all the variables that are not on the tabu list. If a solution better than the current best solution is found, change its value, place it on the tabu list, update E(x) , update the tabu list, and go to Step 1.
step 4b. Find the index i corresponding to the greatest value of E(x_i), change its value of x_i, place it on the tabu list to update the tabu list, update E(x).
step 4c. If this is the fifteenth iteration in the Construction() procedure, go to Step 1.
step 4d. Test if there is any variable that is not on the tabu list and can improve the solution. If not, go to Step 3a. If yes, go to Step 4a.

The randChange() procedure is invoked occasionally and randomly to select an x for the Destruction() using a random number generator. There is less than a 2% probability of invoking after the Construction() procedure. To get the 2% probability, a random number generator is used to create an integer between 1 to 1000. If the value of integer is smaller than 20, the randChange() is invoked. The variable chosen in the randChange() will lead to the change of E(x) for Destruction().

Any local search algorithm, e.g., Algorithm 3 or 4, can be used in Step 1 of this simple tabu search heuristic. However, a limited preliminary implementation of Algorithm 3 and 4 within the Algorithm 5 suggested that due to its simplicity of implementation and computational saving time, the Algorithm 4 with slight modification was quite effective, thus we used it in Step 1 of the Algorithm 5. The slight modification was as follows. If the solution found by a 1-flip is worse than the current best-found solution, quit the local search and go to Step 2.

In order to determine whether the hybrid r-flip/1-flip local search algorithms with two strategies (Algorithms 3 and 4) do better than the hybrid r-flip/1-flip local search embedded with a simple tabu search implementation, we compared Algorithms 3 and 4 to Algorithm 5.

The goal of the new strategies is to reach local optimality on large scale instances with less computing time. We report the comparison of three algorithms of a 2-flip on very-large-scale QUBO instances in the next section.

3. Computational results

In this study, we perform substantial computational experiments to evaluate the proposed strategies for problem size, density, and r value. We compare the performance of Algorithms 3, 4, and 5 for r=2 on very-large-scale QUBO instances. We also compare the best algorithm among Algorithms 3, 4, and 5 to one of the best algorithms for

x^{T} Q x

, i.e., Palubeckis’s multiple start tabu search. We code the algorithms in C++ programming language.

In [29], there are five multiple start tabu search algorithms, and MST2 algorithm had the best results reported by the author. We choose MST2 algorithm with the default values for the parameters recommended by the author [29]. In MST2 algorithm, the number of iterations as the stopping criteria for the first tabu search start subroutine is 25000 * size of problem, then MST2 algorithm reduces the number of iterations to 10000*size of problem as the stopping criteria for the subsequent tabu search starts. Within the tabu search subroutine, if an improved solution is found, then MST2 algorithm invokes a local search immediately. The CPU time limit in MST2 algorithm is checked at the end of the tabu search start subroutine. Thus, the computing time might exceed the CPU time limit for large instances when we choose short CPU time limits.

All algorithms in this study are compiled by GNU C++ compiler v4.8.5 and run on a single core of Intel Xeon Quad-core E5420 Harpertown processors, which have a 2.5 GHz CPU with 8 GB memory. All computing jobs were submitted through the Open PBS Job Management System to ensure both methods using the same CPU for memory usage and CPU time limits on the same instance.

Preliminary results indicated that Algorithms 3, 4, and 5 perform well on instances with the size less than 3,000 and low density. All algorithms found the best-known solution with the CPU time limit of 10 seconds. Thus, we only compare the results of large instances with high density and size from 3,000 to 8,000 by MST2 algorithm and the best algorithm among Algorithms 3, 4, and 5. These benchmark instances with the size from 3,000 to 8,000 have been reported by other researchers [6,30]. In addition, we generate some very-large-scale QUBO instances with high density and size of 30,000 using the same parameters from the benchmark instances. We use a CPU time limit of 600 seconds and r=2 for Algorithms 3, 4, and 5 on the very-large-scale instances in Table 2. We adopted the following notation for computational results:

OFV The value of the objective function for the best solution found by each algorithm.

BFS Best found solution among algorithms within the CPU time limit.

TB[s] Time to reach the best solution in seconds of each algorithm.

AT[s] Average computing time out of 10 runs to reach OFV.

DT %Deviation of computing time out of 10 runs to reach OFV.

Table 2 shows the results of comparison for Algorithms 3, 4, and 5 on very-large-scale instances out of 10 runs. Algorithm 5 produces a better solution than Algorithms 3 and 4 with r=2; thus, we use Algorithm 5 with r=1 and r=2 to compare to MST2 algorithm. We impose a CPU time limit of 60 seconds and 600 seconds per run with 10 runs per instance on Algorithm 5 and MST2 algorithm. We choose tabu tenure value of 100 for 1-flip and 2-flip. The instance data and solutions files are available on this data repository1.

In our implementation, we choose the CPU time limit as the stopping criteria and check the CPU time limit before invoking the tabu search in Algorithm 5. Because MST2 algorithm and Algorithm 5 are not single point-based search methods, the choice of the CPU time limit as the stopping criteria seems to be a fair performance comparison method between algorithms.

Table 3 describes the size and density of each instance and the number of times out of 10 runs to reach the OFV as well as solution deviation within the CPU time limit for MST2 algorithm and Algorithm 5 with r=1 and r=2. MST2 algorithm produces a stable performance and reaches the same OFV frequently out of 10 runs. Algorithm 5 starts from a random initial solution and can search a more diverse solution space in a short CPU time limit. When the CPU time limit is changed to 600 seconds, MST2 algorithm and Algorithm 5 produce a better solution quality in terms of relative standard deviation[31]. The relative standard deviation (RSD) in Table 3 inside the parenthesis is measured by:

R S D = 100 \frac{σ}{μ}

σ = \sqrt{\frac{{\sum (f (x) - \bar{f (x)})}^{2}}{n}}

and

μ = \bar{f (x)}

, where f(x) is the OFV of each run and

\bar{f (x)}

is the mean value of OFV out of n=10 runs. For some instances, the relative standard deviation (RSD) is less than 5.0E-4 even though not all runs found the same OFV. We use 0.000 as the value of RSD when the value is rounded up to three decimal points.

Table 4 reports the computational results of a CPU time limit of 60 seconds, and Table 5 reports the computational results of a CPU time limit of 600 seconds. In Table 4, MST2 algorithm matches 5 out of 27 best solutions within the CPU time limit. The 1-flip strategy in Algorithm 5 matches 26 out of 27 best solutions while the 2-flip strategy in Algorithm 5 matches 18 out of 27 best solutions. For MST2 algorithm, the computing time to find the initial solution exceeded the CPU time limit of 60 seconds for two large instances.

When the CPU time limit is increased to 600 seconds, MST2 algorithm matches 10 out of 27 best solutions. The 1-flip strategy matches 25 out of 27 best solutions, and the 2-flip strategy matches 23 out of 27 best solutions. The 1-flip and 2-flip strategies in Algorithm 5 perform well on the high-density large instances. There is no clear pattern that the 2-flip strategy uses more time than a 1-flip strategy on finding the same OFV. The 1-flip and 2-flip strategies in Algorithm 5 choose the initial solution randomly and independently. The 1-flip strategy has a better performance when the CPU time limits are 60 and 600 seconds.

Table 6 and Table 7 present the time deviation of each algorithm on reaching the OFV for each instance. MST2 algorithm has less variation in computing time when it finds the same OFV while the r-flip strategy in Algorithm 5 has a wider range of computing time. If the algorithm only finds the OFV once out of 10 runs, the time deviation will be zero.

The r-flip strategy can be embedded in other local search heuristics as an improvement procedure. The clever implementation of the r-flip strategy can reduce the computing time as well as improve the solution quality. We reported the time and solutions out of 10 runs for each instance. The time deviation and solution deviation of 10 runs with the short CPU time limits are computed due to the computing resources available to this study.

4. Conclusion

In this study, we explored the Quadratic Unconstrained Binary Optimization (QUBO) problem and introduced significant findings. We established a necessary and sufficient condition for the local optimality of an r-flip search after a 1-flip search has already achieved local optimality. Our computational experiments demonstrated a substantial reduction in candidate options for r-flip implementation. The new r-flip strategy efficiently solved extremely large QUBO instances within 600 seconds, outperforming MST2 in terms of time taken to reach the best-known solutions on benchmark instances. These results are particularly promising for implementing variable neighborhood strategies on extensive problems or sparse matrices.

Author Contributions

Conceptualization, B. Alidaee and H. Wang; methodology, B. Alidaee; validation, B. Alidaee, H. Wang and L. Sua; formal analysis, H. Wang; investigation, H. Wang; data curation, H. Wang; writing—original draft preparation, B. Alidaee; writing—review and editing, H. Wang and L. Sua; visualization, B. Alidaee; All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The instance data and solutions files are available at: https://doi.org/10.18738/T8/WDFBR5.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A. Numerical example for Theorem 1 and Proposition 1

Numerical Example. Consider the problem with Q matrix given below, Figure A1. A locally optimal solution x with f(x)=1528 and the vector of derivatives, E(x) is shown in Figure A2. For r=2, M=100, every two elements that satisfy Proposition 1 is shown in Figure A3. However, only improving two elements are those shown in red font in Figure A4. These are the two elements that satisfy Theorem 1. The new results of f(x) and E(x) on the two elements are shown in Figure A4. In this simple problem, instead of comparing 380 two element combination we only need to compare 21 combinations. Out of these 21 combinations we have 6 possible improving combinations in Figure A5.

Figure A1. Matrix Q, for maximizing

x^{T} Q x

Figure A1. Matrix Q, for maximizing

x^{T} Q x

Figure A2. A locally optimal solution, x(.), and values of E(x_i),i=1,…,n.

In Figure A1 and Figure A2, we have:

E(x₁)= -30 + (50*1 - 68*0 - 66*0 + 94*1 - 100*0 - 20*1 - 4*0 + 80*0 + 94*1 + 0*0 + 86*1 + 76*1 + 98*1 - 36*0 + 76*1 + 52*1 - 8*0 + 48*1 + 42*0) = 624

E(x₂)= -7 + 50*1 + (50*0 + 28*0 + 60*1 + 56*0 - 54*1 + 78*0 + 4*0 + 30*1 - 42*0 - 28*1 - 50*1 + 90*1 - 42*0 + 90*1 - 26*1 + 6*0 + 22*1 + 0*0) = 177

E(x₃) = -26 + (-68*1 + 50*1) + (-46*0 + 26*1 + 82*0 - 50*1 - 90*0 + 44*0 - 94*1 + 54*0 + 52*1 -50*1 + 54*1 - 68*0 - 52*1 + 20*1 + 96*0 + 64*1 - 22*0) = -74

E(x₄) = -35 + (-66*1 + 28*1 - 46*0) + (8*1 – 82* 0 - 84*1 - 46*0 - 34*0 – 22*1 - 62*0 – 34*1 – 56*1 – 86*1 + 18*0 + 44*1 – 84*1 + 2*0 – 72*1 – 60*0) = -459

… for E(x₅) to E(x₁₉)

E(x₂₀) =22+ (42*1+0*1-22*0-60*0+14*1+2*0-70*1+94*0-16*0+0*1+52*0+28*1-96*1+64*1-34*0+10*1-26*1+80*0-54*1)= - 66

f(x₁)= 1*( -30 + 50 + 94 – 20 + 94 + 86 + 76 + 98 + 76 + 52 + 48)=624

f(x₂)= 1*( -7 + 60 – 54 + 30 – 28 – 50 + 90 + 90 – 26 + 22)=127

f(x₃)= 0*(26 – 50 – 94 + 52 – 50 + 54 – 52 + 20 + 64)=0

… for f(x₄) to f(x₁₉)

f(x₂₀)=0*(22)=0

f(x) = 624 + 127 + 55 + 70 + 383 + 3 + 58 + 104 + 89 – 5 + 20 = 1528

Figure A3. Indicated elements satisfy Proposition 1. Only indicated two elements with red font can improve f(x), which satisfy Theorem 1.

Figure A4. An improvement using 2-flip for the locally optimal solution, x, and values of E(x_i),i=1,…,n.

In Figure A1 and Figure A4, we have:

f(x₁)= 1*( - 30 + 50 + 94 – 4 + 94 + 86 + 76 + 98 – 36 + 76 + 52 + 48 + 42)=646

f(x₂)= 1*(-7 + 60 + 78 + 30 – 28 – 50 + 90 – 42 + 90 – 26 + 22)=217

…for f(x₃) to f(x₁₈)

f(x₁₉)=1*(20-54)=-34

f(x₂₀)=1*(22)=22

f(x) = 646 + 217 + 121 + 35 + 315 + 83 + 10 + 240 – 39 + 99 – 31 – 34 + 22=1684

Figure A5. All 6 possible improving combinations for the locally optimal solution (f(x)=1528).

Table A1. An exhaustive search of r-flip rules for QUBO.

Study	r-flip rules
Alidaee, B., G. Kochenberger, and H. Wang, Int. J. Appl. Metaheuristic Comput., 2010. [12]	Proved several theoretical results for r-flip moves in the general psudo-boolean optimization including QUBO.
Anacleto, E, Meneses, C, Ravelo, S, Computers & Operations Research, 2020. [13]	Two closed-form formulas for evaluating r-flip moves was presented. Effectiveness of the moves were evaluated experimentally.
Anacleto, E, Meneses, C, and Liang, T, Computers & Operations Research, 2021. [14]	Considered r-flip move strategy for quadratic assignment problem that can be transferred to QUBO. Closed form formula as well as experimental evaluations considered.
Debevre, P, Sugimura, M, and Parizy, M, Ieee Access, 2023. [3]	Formulated the automotive paint shop problem as QUBO then 1 and several flips moves provided for solution.
Glover, F, and Hao, J-K, Int. J. Metaheuristics, 2010. [7]	Efficiently evaluating of 2-flip moves for QUBO was presented.
Glover, F, and Hao, J-K, Annals of Operations Research, 2016. [8]	A class of approaches called f-flip strategies that include fractional value for f is provided for QUBO.
Katayama, K, and Naihisa, H, in: W. Hart, N. Krasnogor, J.E. Smith (Eds.), Recent Advances in Memetic Algorithms, Springer, Berlin, 2004. [32]	k-flip move in the context of a memetic algorithm was presented.
Liang, R.N, Anacleto, E.A.J, and Meneses, C.N. Computers & Operations Research, 2023. [33]	A closed-form formulae for psuedo boolean optimization as well as data structure for efficient implementation of 1-flip rule presented.
Lozano, M, Molina, D, and Garcia-Martinez, C, European Journal of Operational Research, 2011. [11]	Considered maximum diversity problem which is a QUBO with added number of variables that must be equal to an integer m. A 2-flip strategy with computational experiment was presented.
Lu, Z., Glover, F., & Hao, J.K. in M. Caserta and S. Voss (Eds.): Metaheuristics International Conference 2011 Post-Conference Book, Chapter 4. [34]	Several combinations of neighborhood structures based on 1 and 2-flip strategy for QUBO within the context of tabu search present.
Merz, P, and Freisleben, B, Journal of Heuristics, 2002. [35]	Provided 1 and several flips strategy for QUBO and provided computational experiment.
Rosenberg, G., Vazifeh, M, Woods, B, and Haber, E, Computational Optimization and Applications, 2016. [30]	A k-flip strategy in the context of quantum annealer.

References

Szeider, S. The parameterized complexity of k-flip local search for SAT and MAX SAT. Discrete Optimization 2011, 8, 139–145. [Google Scholar] [CrossRef]
Boros, E.; Hammer, P.L.; Minoux, M.; Rader, D.J. Optimal cell flipping to minimize channel density in VLSI design and pseudo-Boolean optimization. Discret. Appl. Math. 1999, 90, 69–88. [Google Scholar] [CrossRef]
Debevere, P.; Sugimura, M.; Parizy, M. Quadratic Unconstrained Binary Optimization for the Automotive Paint Shop Problem. IEEE Access 2023, 11, 97769–97777. [Google Scholar] [CrossRef]
Glover, F.; Kochenberger, G.; Du, Y. Quantum Bridge Analytics I: a tutorial on formulating and using QUBO models. 4OR 2019, 17, 335–371. [Google Scholar] [CrossRef]
Glover, F.; Kochenberger, G.A.; Alidaee, B. Adaptive Memory Tabu Search for Binary Quadratic Programs. Manag. Sci. 1998, 44, 336–345. [Google Scholar] [CrossRef]
Glover, F.; Lü, Z.; Hao, J.-K. Diversification-driven tabu search for unconstrained binary quadratic problems. 4OR 2010, 8, 239–253. [Google Scholar] [CrossRef]
Glover, F.; Hao, J.K. Fast two-flip move evaluations for binary unconstrained quadratic optimisation problems. Int. J. Metaheuristics 2010, 1, 100. [Google Scholar] [CrossRef]
Glover, F.; Hao, J.-K. f-Flip strategies for unconstrained binary quadratic programming. Ann. Oper. Res. 2015, 238, 651–657. [Google Scholar] [CrossRef]
Kochenberger, G.; Hao, J.-K.; Glover, F.; Lewis, M.; Lü, Z.; Wang, H.; Wang, Y. The unconstrained binary quadratic programming problem: a survey. J. Comb. Optim. 2014, 28, 58–81. [Google Scholar] [CrossRef]
Kochenberger, G.A.; Glover, F.; Wang, H. Binary Unconstrained Quadratic Optimization Problem. In Handbook of Combinatorial Optimization; Pardalos, P.M., Du, D.-Z., Graham, R.L., Eds.; Springer: New York, NY, USA, 2013; pp. 533–557. [Google Scholar]
Lozano, M.; Molina, D.; Garcı´a-Martı´nez, C. Iterated greedy for the maximum diversity problem. Eur. J. Oper. Res. 2011, 214, 31–38. [Google Scholar] [CrossRef]
Alidaee, B.; Kochenberger, G.; Wang, H. Theorems Supporting r-flip Search for Pseudo-Boolean Optimization. Int. J. Appl. Metaheuristic Comput. 2010, 1, 93–109. [Google Scholar] [CrossRef]
Anacleto, E.A.; Meneses, C.N.; Ravelo, S.V. Closed-form formulas for evaluating r-flip moves to the unconstrained binary quadratic programming problem. Comput. Oper. Res. 2019, 113, 104774. [Google Scholar] [CrossRef]
Anacleto, E.; Meneses, C.; Liang, T. Fast r-flip move evaluations via closed-form formulae for Boolean quadraticprogramming problems with generalized upper bound constraints. Computers & Operations Research 2021, 132, 105297. [Google Scholar]
Glover, F.; Lewis, M.; Kochenberger, G. Logical and inequality implications for reducing the size and difficulty of quadratic unconstrained binary optimization problems. Eur. J. Oper. Res. 2018, 265, 829–842. [Google Scholar] [CrossRef]
Lewis, M.; Glover, F. Quadratic unconstrained binary optimization problem preprocessing: Theory and empirical analysis. Networks 2017, 70, 79–97. [Google Scholar] [CrossRef]
Vredeveld, T.; Lenstra, J.K. On local search for the generalized graph coloring problem. Oper. Res. Lett. 2003, 31, 28–34. [Google Scholar] [CrossRef]
Kochenberger, G.; Glover, F. Introduction to special xQx issue. J. Heuristics 2013, 19, 525–528. [Google Scholar] [CrossRef]
Ahuja, R.K.; Ergun, *!!! REPLACE !!!*; Orlin, J.B.; Punnen, A.P. A survey of very large-scale neighborhood search techniques. Discret. Appl. Math. 2002, 123, 75–102. [Google Scholar] [CrossRef]
Ahuja, R.K.; Orlin, J.B.; Pallottino, S.; Scaparra, M.P.; Scutellà, M.G. A Multi-Exchange Heuristic for the Single-Source Capacitated Facility Location Problem. Manag. Sci. 2004, 50, 749–760. [Google Scholar] [CrossRef]
Yagiura, M.; Ibaraki, T. Analyses on the 2 and 3-Flip Neighborhoods for the MAX SAT. J. Comb. Optim. 1999, 3, 95–114. [Google Scholar] [CrossRef]
Yagiura, M.; Ibaraki, T. Efficient 2 and 3-Flip Neighborhood Search Algorithms for the MAX SAT: Experimental Evaluation. J. Heuristics 2001, 7, 423–442. [Google Scholar] [CrossRef]
Yagiura, M.; Kishida, M.; Ibaraki, T. A 3-flip neighborhood local search for the set covering problem. Eur. J. Oper. Res. 2006, 172, 472–499. [Google Scholar] [CrossRef]
Alidaee, B. Fan-and-Filter Neighborhood Strategy for 3-SAT Optimization, Hearin Center for Enterprise Science, T.U.o. Mississippi, Editor. 2004, The University of Mississippi: Hearin Center for Enterprise Science.
Glover, F. A template for scatter search and path relinking; Springer: Berlin/Heidelberg, Germany, 1998. [Google Scholar]
Ivanov, S.V.; Kibzun, A.I.; Mladenović, N.; Urošević, D. Variable neighborhood search for stochastic linear programming problem with quantile criterion. J. Glob. Optim. 2019, 74, 549–564. [Google Scholar] [CrossRef]
Mladenović, N.; Hansen, P. Variable neighborhood search. Computers & Operations Research 1997, 24, 1097–1100. [Google Scholar]
Alidaee, B.; Sloan, H.; Wang, H. Simple and fast novel diversification approach for the UBQP based on sequential improvement local search. Comput. Ind. Eng. 2017, 111, 164–175. [Google Scholar] [CrossRef]
Palubeckis, G. Multistart Tabu Search Strategies for the Unconstrained Binary Quadratic Optimization Problem. Ann. Oper. Res. 2004, 131, 259–282. [Google Scholar] [CrossRef]
Rosenberg, G.; Vazifeh, M.; Woods, B.; Haber, E. Building an iterative heuristic solver for a quantum annealer. Comput. Optim. Appl. 2016, 65, 845–869. [Google Scholar] [CrossRef]
Glen, S. Relative Standard Deviation: Definition & Formula. 2014. Available online: https://www.statisticshowto.com/relative-standard-deviation/.
Katayama, K, and Naihisa, H, An evolutionary approach for the maximum diversity problem, in: W. Hart, N. Krasnogor, J.E. Smith (Eds.), Recent Advances in Memetic Algorithms, Springer: Berlin, 2004, pp. 31–47.
Liang, R.N.; Anacleto, E.A.; Meneses, C.N. Fast 1-flip neighborhood evaluations for large-scale pseudo-Boolean optimization using posiform representation. Comput. Oper. Res. 2023, 159, 106324. [Google Scholar] [CrossRef]
Lu, Z., Glover, F., & Hao, J.K. Neighborhood combination for unconstrained binary quadratic problems, in M. Caserta and S. Voss (Eds.): Metaheuristics International Conference 2011 Post-Conference Book, Chapter 4, 2014; pp. 49–61.
Merz, P.; Freisleben, B. Greedy and Local Search Heuristics for Unconstrained Binary Quadratic Programming. J. Heuristics 2002, 8, 197–213. [Google Scholar] [CrossRef]

1	https://doi.org/10.18738/T8/WDFBR5

Figure 1. Pseudo-code for a 1-flip local search subroutine for maximization problems.

Figure 2. Pseudo-code for an exhaustive r-flip local search subroutine for maximization problems.

Figure 3. Pseudo-code for hybrid r-flip/1-flip local search, Strategy 1, for maximization problems.

Figure 4. Pseudo-code for hybrid r-flip/1-flip local search, Strategy 2, for maximization problems.

Figure 5. Pseudo-code for hybrid r-flip/1-flip local search embedded with a simple tabu search algorithm.

Table 1. Size of the set D(1).

r=2

r=3

r=4

Density

Prob. Size

0.1

0.3

0.5

0.8

0.1

0.3

0.5

0.8

0.1

0.3

0.5

0.8

2500

<100

<40

<30

<20

<400

<200

<100

<1000

<500

<300

<200

3000

<100

<40

<30

<20

<400

<200

<100

<1100

<500

<400

<250

4000

<100

<30

<20

<500

<200

<100

<1200

<600

<400

<250

5000

<100

<30

<20

<500

<200

<100

<1300

<600

<400

<250

6000

<100

<30

<20

<500

<200

<100

<1400

<600

<400

<250

Table 2. Results of Algorithms 3, 4 and 5 on p30000 instances with the CPU time limit of 600 seconds and r=2.

Instance ID	size	density	Algorithm 3		Algorithm 4		Algorithm 5
Instance ID	size	density	OFV	TB[s]	OFV	TB[s]	OFV	TB[s]
p30000_1	30000	0.5	127239168	591	127292467	591	127336719	592
p30000_2	30000	0.8	158439036	572	158472098	555	158526518	571
p30000_3	30000	1	179192241	584	179219781	587	179261723	590

Table 3. The solution quality of MST2 algorithm and Algorithm 5 with 60- and 600-seconds time CPU limits out of 10 runs.

Instance ID	size	density	MST2 with 60s	r-flip with 60s		MST2 with 600s	r-flip with 600s
Instance ID	size	density	MST2 with 60s	r=1	r=2	MST2 with 600s	r=1	r=2
p3000_1	3000	0.5	10(0)	10(0)	10(0)	10(0)	10(0)	10(0)
p3000_2	3000	0.8	10(0)	10(0)	10(0)	10(0)	10(0)	10(0)
p3000_3	3000	0.8	4(0.01)	7(0.007)	10(0)	10(0)	10(0)	10(0)
p3000_4	3000	1	10(0)	10(0)	10(0)	10(0)	10(0)	10(0)
p3000_5	3000	1	10(0)	9(0.003)	7(0.002)	9(0.001)	10(0)	10(0)
p4000_1	4000	0.5	10(0)	10(0)	10(0)	10(0)	10(0)	10(0)
p4000_2	4000	0.8	10(0)	10(0)	10(0)	9(0.004)	10(0)	10(0)
p4000_3	4000	0.8	10(0)	10(0)	10(0)	10(0)	10(0)	10(0)
p4000_4	4000	1	1(0.033)	10(0)	10(0)	10(0)	10(0)	10(0)
p4000_5	4000	1	10(0)	10(0)	10(0)	10(0)	10(0)	10(0)
p5000_1	5000	0.5	6(0.000)	1(0.002)	2(0.002)	10(0)	3(0.002)	2(0.002)
p5000_2	5000	0.8	10(0)	4(0.003)	1(0.002)	6(0.012)	10(0)	10(0)
p5000_3	5000	0.8	10(0)	7(0.001)	3(0.002)	10(0)	10(0)	10(0)
p5000_4	5000	1	10(0)	1(0.002)	1(0.001)	10(0)	3(0.002)	1(0.001)
p5000_5	5000	1	6(0.021)	9(0.003)	4(0.004)	10(0)	10(0)	10(0)
p6000_1	6000	0.5	10(0)	10(0)	4(0.001)	10(0)	10(0)	10(0)
p6000_2	6000	0.8	10(0)	4(0.001)	4(0.001)	1(0.006)	10(0)	9(0)
p6000_3	6000	1	9(0.002)	3(0.002)	1(0.007)	10(0)	10(0)	10(0)
p7000_1	7000	0.5	1(0.002)	1(0.006)	1(0.007)	10(0)	2(0.002)	4(0.002)
p7000_2	7000	0.8	7(0.000)	1(0.008)	1(0.008)	10(0)	1(0.004)	2(0.004)
p7000_3	7000	1	8(0.011)	3(0.021)	5(0.023)	10(0)	10(0)	10(0)
p8000_1	8000	0.5	10(0)	1(0.004)	1(0.005)	9(0.001)	10(0)	1(0.002)
p8000_2	8000	0.8	10(0)	1(0.009)	1(0.008)	10(0)	7(0.003)	10(0)
p8000_3	8000	1	10(0)	1(0.013)	1(0.01)	10(0)	4(0.001)	3(0.002)
p30000_1	30000	0.5	1(0.002)	1(0.023)	1(0.019)	7(0.018)	1(0.017)	1(0.011)
p30000_2	30000	0.8	10(0)	1(0.017)	1(0.016)	6(0.01)	1(0.019)	1(0.013)
p30000_3	30000	1	10(0)	1(0.015)	1(0.019)	2(0.037)	1(0.025)	1(0.019)

Table 4. Results of MST2 algorithm and r-flip strategy in Algorithm 5 within the CPU time limit of 60 seconds.

Instance ID	BFS (60s)	MST2 (60s)		r-flip (60s)
Instance ID	BFS (60s)	OFV	TB[s]	OFV(r=1)	TB[s]	OFV(r=2)	TB[s]
p3000_1	3931583	3931583	10	3931583	3	3931583	8
p3000_2	5193073	5193073	25	5193073	2	5193073	2
p3000_3	5111533	5111533	52	5111533	8	5111533	4
p3000_4	5761822	5761437	10	5761822	2	5761822	2
p3000_5	5675625	5675430	24	5675625	7	5675625	17
p4000_1	6181830	6181830	40	6181830	3	6181830	4
p4000_2	7801355	7797821	12	7801355	13	7801355	4
p4000_3	7741685	7741685	31	7741685	5	7741685	8
p4000_4	8711822	8709956	58	8711822	5	8711822	11
p4000_5	8908979	8905340	27	8908979	4	8908979	13
p5000_1	8559680	8556675	56	8559680	21	8559680	7
p5000_2	10836019	10829848	34	10836019	59	10836019	11
p5000_3	10489137	10477129	28	10489137	20	10489137	16
p5000_4	12251710	12245282	52	12251710	54	12251520	42
p5000_5	12731803	12725779	56	12731803	17	12731803	16
p6000_1	11384976	11377315	42	11384976	12	11384976	5
p6000_2	14333855	14330032	39	14333855	27	14333767	14
p6000_3	16132915	16122333	51	16130731	24	16132915	48
p7000_1	14477949	14467157	56	14477949	41	14476263	21
p7000_2	18249948	18238729	55	18249948	47	18246895	47
p7000_3	20446407	20431354	59	20446407	15	20446407	12
p8000_1	17340538	17326259	47	17340538	26	17340538	35
p8000_2	22208986	22180465	55	22208986	54	22208683	53
p8000_3	24670258	24647248	56	24670258	43	24669351	50
p30000_1	127252438	126732483	60	127252438	58	127219336	60
p30000_2	158384175	157481366	69	158384175	59	158339497	60
p30000_3	179103085	178093109	89	179103085	58	179029747	54

Table 5. Results of MST2 algorithm and r-flip strategy in Algorithm 5 within the CPU time limit of 600 seconds.

Instance ID	BFS (600s)	MST2 (600s)		r-flip (600s)
Instance ID	BFS (600s)	OFV	TB[s]	OFV(r=1)	TB[s]	OFV(r=2)	TB[s]
p3000_1	3931583	3931583	11	3931583	5	3931583	5
p3000_2	5193073	5193073	25	5193073	1	5193073	3
p3000_3	5111533	5111533	52	5111533	30	5111533	8
p3000_4	5761822	5761822	269	5761822	1	5761822	2
p3000_5	5675625	5675625	505	5675625	43	5675625	29
p4000_1	6181830	6181830	40	6181830	4	6181830	2
p4000_2	7801355	7800851	530	7801355	8	7801355	8
p4000_3	7741685	7741685	30	7741685	5	7741685	2
p4000_4	8711822	8711822	67	8711822	2	8711822	7
p4000_5	8908979	8906525	65	8908979	4	8908979	13
p5000_1	8559680	8559075	324	8559680	9	8559680	27
p5000_2	10836019	10835437	541	10836019	17	10836019	21
p5000_3	10489137	10488735	400	10489137	29	10489137	38
p5000_4	12252318	12249290	265	12252318	127	12251848	143
p5000_5	12731803	12731803	265	12731803	19	12731803	32
p6000_1	11384976	11384976	406	11384976	8	11384976	39
p6000_2	14333855	14333767	498	14333855	62	14333855	17
p6000_3	16132915	16128609	239	16132915	60	16132915	71
p7000_1	14478676	14477039	344	14478676	92	14478676	397
p7000_2	18249948	18242205	587	18249948	115	18249844	43
p7000_3	20446407	20431833	109	20446407	47	20446407	21
p8000_1	17341350	17337154	546	17340538	45	17341350	141
p8000_2	22208986	22207866	122	22208986	49	22208986	89
p8000_3	24670924	24669797	402	24670924	185	24670924	386
p30000_1	127336719	127323304	568	127332912	598	127336719	592
p30000_2	158561564	158438942	573	158561564	580	158526518	571
p30000_3	179329754	179113916	575	179329754	599	179261723	590

Table 6. Computing the time deviation of MST2 algorithm and r-flip strategy in Algorithm 5 within the time limit of 60 seconds.

Instance ID	MST2		1-flip		2-flip
Instance ID	AT[s]	DT	AT[s]	DT	AT[s]	DT
p3000_1	12.5	15.663	15.7	84.075	24.1	56.875
p3000_2	32.5	20.059	4.9	45.583	4.8	68.606
p3000_3	54.3	5.901	29.3	46.180	12.0	60.477
p3000_4	13.3	18.434	12.0	107.798	10.0	51.640
p3000_5	30.4	15.829	33.2	45.470	32.3	48.240
p4000_1	49.0	10.227	7.7	50.131	9.3	36.570
p4000_2	14.2	9.848	22.1	62.351	21.1	70.476
p4000_3	35.4	11.236	15.1	69.699	16.5	75.542
p4000_4	58.0	0	22.2	65.408	35.4	45.780
p4000_5	31.1	11.082	18.1	73.038	29.6	40.109
p5000_1	56.8	2.339	4.0	0	10.0	42.426
p5000_2	35.0	3.563	28.3	74.329	11.0	0
p5000_3	30.0	8.607	38.9	33.038	33.0	50.069
p5000_4	54.0	5.238	54.0	0	42.0	0
p5000_5	57.2	1.317	38.8	40.504	30.5	63.379
p6000_1	43.3	5.112	32.9	47.380	21.8	67.507
p6000_2	39.8	4.238	31.8	51.521	42.3	46.315
p6000_3	52.1	5.027	32.3	44.640	48.0	0
p7000_1	56.0	0	41.0	0	21.0	0
p7000_2	55.3	1.367	47.0	0	47.0	0
p7000_3	59.0	0	42.0	56.293	35.2	45.958
p8000_1	48.1	7.232	26.0	0	35.0	0
p8000_2	55.9	0.566	54.0	0	53.0	0
p8000_3	57.2	1.806	43.0	0	50.0	0
p30000_1	60.0	0	58.0	0	60.0	0
p30000_2	70.0	1.166	59.0	0	60.0	0
p30000_3	90.3	0.912	58.0	0	54.0	0

Table 7. Computing the time deviation of MST2 algorithm and r-flip strategy in Algorithm 5 within the time limit of 600 seconds.

Instance ID	MST2		1-flip		2-flip
Instance ID	AT[s]	DT	AT[s]	DT	AT[s]	DT
p3000_1	11.9	16.067	21.3	62.678	37.8	130.045
p3000_2	29.1	18.144	5.5	69.234	7.7	81.231
p3000_3	58.5	16.889	126.4	99.610	143.6	116.812
p3000_4	292.1	11.285	10.8	58.365	13.7	49.512
p3000_5	543.9	6.365	109.1	88.328	164.3	72.144
p4000_1	42.8	7.773	13.5	63.553	15.1	63.861
p4000_2	552.2	2.597	30.7	49.923	37.0	110.712
p4000_3	31.7	7.293	18.8	57.442	22.9	47.545
p4000_4	70.8	4.094	42.2	102.331	48.7	115.376
p4000_5	70.5	8.596	25.3	81.984	43.0	69.595
p5000_1	337.2	4.265	70.7	126.287	48.0	61.872
p5000_2	557.8	4.565	135.4	96.462	210.6	67.670
p5000_3	428.5	7.257	115.8	96.524	115.5	104.238
p5000_4	279.4	5.987	270.3	48.842	143.0	0
p5000_5	287.1	9.234	194.5	94.641	172.7	92.268
p6000_1	424.8	4.555	152.9	95.641	145.9	46.657
p6000_2	498.0	0	142.5	97.375	73.8	122.070
p6000_3	252.3	5.571	248.0	51.129	318.1	61.672
p7000_1	344.5	0.153	272.5	93.675	441.5	12.974
p7000_2	587.0	0	115.0	0	265.1	76.694
p7000_3	109.0	0	84.5	39.472	131.1	55.401
p8000_1	548.6	1.398	251.3	63.346	141.0	0
p8000_2	145.4	11.829	258.3	56.352	300.7	56.001
p8000_3	514.3	11.365	368.8	43.667	417.3	12.797
p30000_1	572.0	1.365	598.0	0	592.0	0
p30000_2	581.5	1.526	580.0	0	571.0	0
p30000_3	586.5	2.773	599.0	0	590.0	0

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.

MDPI Initiatives

Important Links

Choose an area of interest and we will send you notifications of new preprints at your preferred frequency.

Disclaimer

An Efficient Closed-form Formula for Evaluating r-flip Moves in Quadratic Unconstrained Binary Optimization

Abstract

1. Introduction

1.1. Previous Works

2. New Results on Closed-form Formulas

2.1. Strategy 1

2.2. Strategy 2

2.3. Implementation Details

3. Computational results

4. Conclusion

Author Contributions

Funding

Data Availability Statement

Conflicts of Interest

Appendix A. Numerical example for Theorem 1 and Proposition 1

References

MDPI Initiatives

Important Links

Subscribe