Update JSR+matprod.bib

JSR+matprod.bib

@@ -4712,17 +4712,17 @@ @ARTICLE{Dai:JFI14
  zblnumber     = "1372.93209",
  doi           = "10.1016/j.jfranklin.2014.01.010",
  url           = "https://www.sciencedirect.com/science/article/pii/S001600321400012X",
- annote        = "In this paper, there are shown the following two statements.
-                  \begin{enumerate} \item[(1)] \emph{A discrete-time
+ annote        = "In this paper, there are shown the following two statements.\par
+                  (1) \emph{A discrete-time
                   Markovian jump linear system is uniformly exponentially
                   stable if and only if it is robustly periodically stable,
-                  by using a Gel'fand--Berger--Wang formula proved here.}
-                  \item[(2)] \emph{A random linear ODE driven by a semiflow
+                  by using a Gel'fand--Berger--Wang formula proved here.}\par
+                  (2) \emph{A random linear ODE driven by a semiflow
                   with closing by periodic orbits property is uniformly
                   exponentially stable if and only if it is robustly
                   periodically stable, by using Shantao Liao's perturbation
-                  technique and the semi-uniform ergodic theorems.}
-                  \end{enumerate} The proofs involve ergodic theory in both
+                  technique and the semi-uniform ergodic theorems.}\par
+                  The proofs involve ergodic theory in both
                   of the above two cases. In addition, counterexamples are
                   constructed to the robustness condition and to spectral
                   finiteness of linear cocycle.",
@@ -5040,15 +5040,15 @@ @MISC{DHX:ArXiv11-2
                   specifically, let $\rho(A)$ stand for the spectral radius
                   of a matrix $A\in\mathbb{R}^{d\times d}$, then the outline
                   of results obtained in this paper
-                  are:\begin{enumerate}\item[(1)] \emph{For the case $d=2$,
+                  are:\par (1) \emph{For the case $d=2$,
                   $\boldsymbol{S}$ is absolutely stable (i.e.,
                   $\|S_{\sigma_{\!n}}\cdots S_{\sigma_{\!1}}\|\to0$ driven by
                   all switching signals $\sigma$) if and only if
                   $\rho(S_1),\rho(S_2)$ and $\rho(S_1S_2)$ all are less than
-                  $1$;}\item[(2)] \emph{For the case $d=3$, $\boldsymbol{S}$
+                  $1$;}\par (2) \emph{For the case $d=3$, $\boldsymbol{S}$
                   is absolutely stable if and only if $\rho(A)<1\;\forall
                   A\in\{S_1,S_2\}^\ell$ for $\ell=1,2,3,4,5,6$, and
-                  $8$.}\end{enumerate} This further implies that for any
+                  $8$.}\par This further implies that for any
                   $\boldsymbol{S}=\{S_1,S_2\}\subset\mathbb{R}^{d\times d}$
                   with the generalized spectral radius
                   $\rho(\boldsymbol{S})=1$ where $d=2$ or $3$, if
@@ -7134,11 +7134,11 @@ @ARTICLE{GSM:IEEETAC07
                   the, convex hull of a set of Metzler matrices is a
                   necessary and sufficient condition for the asymptotic
                   stability of the associated switched linear system under
-                  arbitrary switching. In this note, we show that (1) this
+                  arbitrary switching. In this note, we show that\par(1) this
                   conjecture is true for systems constructed from a pair of
-                  second-order Metzler matrices; (2) the conjecture is true
+                  second-order Metzler matrices;\par(2) the conjecture is true
                   for systems constructed from an arbitrary finite number of
-                  second-order Metzler matrices; and (3) the conjecture is in
+                  second-order Metzler matrices; and\par(3) the conjecture is in
                   general false for higher order systems. The implications of
                   our results, both for the design of switched positive
                   linear systems, and for research directions that arise as a
@@ -16138,24 +16138,24 @@ @MISC{JSRToolbox
                   needed, for instance by setting a maximal computation time
                   or by fine-tuning a particular step in an algorithm. The
                   main steps of the default algorithm are the following:
-                  \begin{itemize} \item Try to transform the problem into a
-                  set of smaller independent problems. This is possible when
-                  the matrices in the set $\Sigma$ are simultaneously
-                  blocktriangularizable. \item If the matrices are
+                  \par$\bullet$ Try to transform the problem into a set of
+                  smaller independent problems. This is possible when the
+                  matrices in the set $\Sigma$ are simultaneously
+                  blocktriangularizable.\par$\bullet$ If the matrices are
                   nonnegative, start with the pruning algorithm in order to
                   get some bounds $[\beta^-, \beta^+]$ on the joint spectral
                   radius, then compute the joint conic radius, using the
                   positive orthant as cone, and the ellipsoidal norm
-                  approximation using $[\beta^-, \beta^+]$ as initial bounds.
-                  \item If some matrices have negative entries, start with a
-                  variant of Gripenberg's algorithm in order to get some
-                  initial bounds and a candidate product. This variant may
-                  rescale the matrices during the computation in order to
-                  avoid overflows. After this first step, compute the
-                  ellipsoidal norm approximation and, if needed, try to
-                  certify optimality or to find a better candidate product
-                  using a balanced complex polytope method or a conitope
-                  method, which is a lifted polytope method. \end{itemize}",
+                  approximation using $[\beta^-, \beta^+]$ as initial
+                  bounds.\par$\bullet$ If some matrices have negative
+                  entries, start with a variant of Gripenberg's algorithm in
+                  order to get some initial bounds and a candidate product.
+                  This variant may rescale the matrices during the
+                  computation in order to avoid overflows. After this first
+                  step, compute the ellipsoidal norm approximation and, if
+                  needed, try to certify optimality or to find a better
+                  candidate product using a balanced complex polytope method
+                  or a conitope method, which is a lifted polytope method.",
 }
 
 @INPROCEEDINGS{VHJ:ACM14,