On construction of upper and lower bounds for the HOMO-LUMO spectral gap

Soña Pavlíková; Daniel Ševčovič

doi:10.3934/naco.2019005

Article Contents

2019, Volume 9, Issue 1: 53-69. Doi: 10.3934/naco.2019005

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On construction of upper and lower bounds for the HOMO-LUMO spectral gap

Soña Pavlíková^1, and
Daniel Ševčovič^2, ,

1.
Institute of Information Engineering, Automation, and Mathematics, FCFT, Slovak Technical University, 812 37 Bratislava, Slovakia
2.
Department of Applied Mathematics and Statistics, FMFI, Comenius University, 842 48 Bratislava, Slovakia

* Corresponding author: Daniel Ševčovič
* Corresponding author: Daniel Ševčovič

Received: January 2018

Revised: June 2018

Published: March 2019

The authors were supported by VEGA grants 1/0142/17(S.P.) and 1/0062/18(D.Š.)

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Abstract

In this paper we study spectral properties of graphs which are constructed from two given invertible graphs by bridging them over a bipartite graph. We analyze the so-called HOMO-LUMO spectral gap which is the difference between the smallest positive and largest negative eigenvalue of the adjacency matrix of a graph. We investigate its dependence on the bridging bipartite graph and we construct a mixed integer semidefinite program for maximization of the HOMO-LUMO gap with respect to the bridging bipartite graph. We also derive upper and lower bounds for the optimal HOMO-LUMO spectral graph by means of semidefinite relaxation techniques. Several computational examples are also presented in this paper.

Keywords:

Mathematics Subject Classification: Primary: 05C50, 15A09, 15B36; Secondary: 90C11, 90C22.

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Figure 1. A bridged graph $G_C = {\mathcal B}_K(G_A, G_B)$ through a bipartite graph $G_K$.

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Figure 2. Simple graphs $G_A$ and $G_B$ (left) and the bridged graph $G_C$ with the maximal HOMO-LUMO spectral gap which can be constructed by bridging $G_A$ and $G_B$ over the vertex 1 of $G_B$ ($k_B = 1$) to the vertices of $G_A$ (right).

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Figure 3. An example of an invertible graph $F_0$ (left) representing the chemical organic molecule of fulvene (right).

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Figure 4. Results of optimal bridging of the fulvene graph G_B= F₀ through the vertices {1,2} to G_A=F₀ a);through the vertices {1,4} to G_A=F₀ b);and through the vertices {1,2} to G_A=F₁ c).

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Figure 5. Results of optimal bridging of the graph $G_B = P_4$ through the vertices $\{1, 3\}$ to $G_A = P_6$ a); through the vertices $\{2, 3\}$ to $G_A = P_6$ b); and through the vertex $\{2\}$ to $G_A = T_4$ c).

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Figure 6. Results of optimal bridging of the graph $G_B = P_4$ through the vertices $\{1, 3\}$ to $G_A = P_6$ a); through the vertices $\{2, 3\}$ to $G_A = P_6$ b); and through the vertex $\{2\}$ to $G_A = T_4$ c); with the constraint of maximal degree equal to 3.

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Table 1. A sample Matlab code for computing mixed integer semidefinite programming problem (12). The output of the program is the optimal value $\Lambda^{opt}_{HL}(G_A, G_B) = \overline{\Lambda}^{sir}_{HL}(G_A, G_B)$.

mu=sdpvar(1); eta=sdpvar(1); W=intvar(m, m); K=binvar(n, m); ops=sdpsettings('solver', 'bnb', 'bnb.maxiter', bnbmaxiter);
Fconstraints=[...
[[W, K'];
[K, eye(n, n)]
]>=0, ...
mu>=0, eta>=0, ...
[[eye(n, n)-muinv(A), Kinv(B)];
[inv(B)K', eye(m, m)-muinv(B) + inv(B)Winv(B)]
] >= 0, ...
[[eye(n, n) + etainv(A), Kinv(B)];
[inv(B)K', eye(m, m) + etainv(B) + inv(B)Winv(B)]
] >= 0, ...
sum(K(:, :))==diag(W)', sum(K(:))>=1, ...
vec(W(:))>=0, 0 < =vec(K(:)) < =1, ...
sum([[A, K]; [K', B]]) < =maxdegree*ones(1, n+m), ...,
K*[zeros(kB, m-kB); eye(m-kB, m-kB)] == zeros(n, m-kB), ...
];

solvesdp(Fconstraints, -mu-eta, ops)

LambdaSIR = double(mu + eta)

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Table 2. The computational results and comparison of various semidefinite relaxations. The first two columns describe the graph $G_A$ and $G_B$ with the chosen set of bridging vertices. The optimal value $\Lambda_{HL}^{opt} = \overline{\Lambda}_{HL}^{sir}$ is shown in bold in the middle column. The upper $\overline{\Lambda}_{HL}^{sdp}$ and lower bounds $\underline{\Lambda}_{HL}^{sdp}$, $\underline{\Lambda}_{HL}^{sir}$ are also presented together with computational times in seconds computed on Quad core Intel 1.5GHz CPU with 4 GB of memory.

$G_A$	$G_B$	$\underline{\Lambda}_{HL}^{sdp}$	$\underline{\Lambda}_{HL}^{sir}$	$\Lambda _{HL}^{opt}$=$\bar \Lambda _{HL}^{sir}$	$\overline{\Lambda}_{HL}^{sdp}$	$G_B \mapsto G_A$

$F_0$	$F_0$	$0.233688$	$0.531664$	$\bf 0.74947$	$0.87214$	$1\mapsto 3, 5;\ \ 2\mapsto 6$
	$(1, 2)$	$(0.27s)$	$(3.38s)$	$(83s)$	$(2.2s)$
$F_0$	$F_0$	$0.333126$	$0.72678$	$\bf 0.85828$	$0.87214$	$1\mapsto \emptyset;\ \ 4\mapsto 3, 5, 6$
	$(1, 4)$	$(0.31s)$	$(4.75s)$	$(36s)$	$(2.2s)$
$F_0$	$F_0$	$0.333126$	$0.719668$	$\bf 0.81389$	$0.87214$	$1\mapsto 4;\ \ 3\mapsto 4$
	$(1, 3)$	$(0.31s)$	$(4.27s)$	$(75s)$	$(2.2s)$
$F_1$	$F_0$	$0.163626$	$0.450022$	$\bf 0.56655$	$0.56666$	$1\mapsto \emptyset; \ \ 2\mapsto 9, 11, 12$
	$(1, 2)$	$(0.28s)$	$(7.65s)$	$(12470s)$	$(2.2s)$
$P_4$	$P_4$	$0.472136$	$0.86953$	$\bf 1.06418$	$1.23607$	$2\mapsto 2, 4;\ \ 3\mapsto 1, 3$
	$(2, 3)$	$(0.27s)$	$(2.18s)$	$(12.6s)$	$(2.2s)$
$P_6$	$P_4$	$0.367365$	$0.811369$	$\bf 0.87366$	$0.89008$	$1\mapsto 4, 6;\ \ 3\mapsto 4, 6$
	$(1, 3)$	$(0.26s)$	$(4.6s)$	$(59s)$	$(2.1s)$
$P_6$	$P_4$	$0.367365$	$0.737641$	$\bf 0.87321$	$0.89008$	$2\mapsto 4, 6;\ \ 3\mapsto 1, 3$
	$(2, 3)$	$(0.26s)$	$(3.41s)$	$(57s)$	$(2.1s)$
$P_{10}$	$P_4$	$0.252282$	$0.523808$	$\bf 0.56837$	$0.56926$	$2\mapsto 8, 10;\ \ 3\mapsto \emptyset$
	$(2, 3)$	$(0.26s)$	$(6.32s)$	$(4109s)$	$(2.6s)$
$T_4$	$P_4$	$0.38832$	$0.73094$	$\bf 0.93258$	$0.95452$	$2\mapsto 3, 8$
	$(2)$	$(0.31s)$	$(1.57s)$	$(12s)$	$(2.31s)$

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Table 3. The computational results and comparison of various relaxations. The chosen graphs and description of columns is the same as in Table 2. In this table we present results of optimization when additional constraint of the maximal degree 3 has been imposed.

$G_A$	$G_B$	$\underline{\Lambda}_{HL}^{sdp}$	$\underline{\Lambda}_{HL}^{sir}$	$\Lambda_{HL}^{opt}=\overline{\Lambda}_{HL}^{sir}$	$\overline{\Lambda}_{HL}^{sdp}$	$G_B \mapsto G_A$

$F_0$	$F_0$	$0.233688$	$0.507678$	$\bf 0.720830$	$0.87214$	$1\mapsto \emptyset;\ 2\mapsto 6$
	$(1, 2)$	$(0.31s)$	$(2.73s)$	$(7.1s)$	$(2.9s)$
$F_0$	$F_0$	$0.233688$	$0.468053$	$\bf0.720830$	$0.87214$	$1\mapsto6;4\mapsto\emptyset$
	$(1, 4)$	$(0.31s)$	$(1.1s)$	$(2.33s)$	$(2.85s)$
$F_0$	$F_0$	$0.333126$	$0.706635$	$\bf0.776875$	$0.87214$	$1\mapsto6;3\mapsto6$
	$(1, 3)$	$(0.35s)$	$(2.45s)$	$(8.4s)$	$(2.82s)$
$F_1$	$F_0$	$0.163626$	$0.389941$	$\bf0.493727$	$0.566658$	$1\mapsto6;2\mapsto\emptyset$
	$(1, 2)$	$(0.38s)$	$(3.67s)$	$(13.4s)$	$(2.83s)$
$P_4$	$P_4$	$0.472136$	$0.869530$	$\bf0.954520$	$1.23607$	$3\mapsto\emptyset;2\mapsto2$
	$(2, 3)$	$(0.31s)$	$(1.86s)$	$(7.8s)$	$(2.86s)$
$P_6$	$P_4$	$0.367365$	$0.811369$	$\bf0.828427$	$0.89008$	$1\mapsto4, 6;3\mapsto2$
	$(1, 3)$	$(0.36s)$	$(3.35s)$	$(22.9s)$	$(2.83s)$
$P_6$	$P_4$	$0.367365$	$0.737641$	$\bf0.820751$	$0.89008$	$2\mapsto5;3\mapsto2$
	$(2, 3)$	$(0.33)$	$(2.73s)$	$(9.21s)$	$(2.87s)$
$P_{10}$	$P_4$	$0.252282$	$0.523808$	$\bf0.559046$	$0.56926$	$2\mapsto\emptyset;3\mapsto11$
	$(2, 3)$	$(0.33s)$	$(4.78s)$	$(13.87s)$	$(2.86s)$
$T_4$	$P_4$	$0.38832$	$0.692266$	$\bf0.890084$	$0.95452$	$2\mapsto4$
	$(2)$	$(0.31s)$	$(0.88s)$	$(1.5s)$	$(2.11s)$

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