Corrected some typos in SURF_lmm documentation.
# However, it is strongly recommended to install a local
# copy of MathJax from http://www.mathjax.org before deployment.
# However, it is strongly recommended to install a local
# copy of MathJax from http://www.mathjax.org before deployment.
-MATHJAX_RELPATH = http://www.mathjax.org/mathjax
+MATHJAX_RELPATH = https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS_HTML
# The MATHJAX_EXTENSIONS tag can be used to specify one or MathJax extension
# names that should be enabled during MathJax rendering.
# The MATHJAX_EXTENSIONS tag can be used to specify one or MathJax extension
# names that should be enabled during MathJax rendering.
/** @addtogroup SURF_lmm
* @details
/** @addtogroup SURF_lmm
* @details
- * A linear maxmin solver to resolves inequations systems.
+ * A linear maxmin solver to resolve inequations systems.
*
* Most SimGrid model rely on a "fluid/steady-state" modeling that
*
* Most SimGrid model rely on a "fluid/steady-state" modeling that
- * samount to share resources between actions at relatively
+ * simulate the sharing of resources between actions at relatively
* coarse-grain. Such sharing is generally done by solving a set of
* linear inequations. Let's take an example and assume we have the
* variables \f$x_1\f$, \f$x_2\f$, \f$x_3\f$, and \f$x_4\f$ . Let's
* say that \f$x_1\f$ and \f$x_2\f$ correspond to activities running
* coarse-grain. Such sharing is generally done by solving a set of
* linear inequations. Let's take an example and assume we have the
* variables \f$x_1\f$, \f$x_2\f$, \f$x_3\f$, and \f$x_4\f$ . Let's
* say that \f$x_1\f$ and \f$x_2\f$ correspond to activities running
- * and the same CPU \f$A\f$ whose capacity is \f$C_A\f$ . In such a
+ * and the same CPU \f$A\f$ whose capacity is \f$C_A\f$. In such a
* case, we need to enforce:
*
* \f[ x_1 + x_2 \leq C_A \f]
* case, we need to enforce:
*
* \f[ x_1 + x_2 \leq C_A \f]
* - bound (set)
* - shared (set)
* - usage (computed)
* - bound (set)
* - shared (set)
* - usage (computed)
* Variable:
* - weight (set)
* - bound (set)
* - value (computed)
* Variable:
* - weight (set)
* - bound (set)
* - value (computed)
* Element:
* - value (set)
*
* Element:
* - value (set)
*
* var1.weight * var1.value * elem1.value + var2.weight * var2.value * elem2.value <= cons1.bound
* var2.weight * var2.value * elem3.value + var3.weight * var3.value * elem4.value <= cons2.bound
*
* var1.weight * var1.value * elem1.value + var2.weight * var2.value * elem2.value <= cons1.bound
* var2.weight * var2.value * elem3.value + var3.weight * var3.value * elem4.value <= cons2.bound
*
- * where `var1.value`, `var2.value` and `var3.value` are the unknown values
+ * where `var1.value`, `var2.value` and `var3.value` are the unknown values.
- * if a constraint is not shared the sum is replace by a max
+ * If a constraint is not shared, the sum is replaced by a max.
+ * For example, a third non-shared constraint `cons3` and the associated elements `elem5` and `elem6` could write as:
+ *
+ * max( var1.weight * var1.value * elem5.value , var3.weight * var3.value * elem6.value ) <= cons3.bound
+ *
+ * This is usefull for the sharing of resources for various models.
+ * For instance, for the network model, each link is associated
+ * to a constraint and each communication to a variable.
- * Its usefull for the sharing of resources for various models.
- * For instance for the network model the link are associated
- * to consrtaint and the communications to variables.
*/
XBT_PUBLIC_DATA(double) sg_maxmin_precision;
*/
XBT_PUBLIC_DATA(double) sg_maxmin_precision;
