From 37ceaf17dbe2c85bff95f1cb33c4550f86b6f843 Mon Sep 17 00:00:00 2001
From: KEXIN CHEN <113996265+KexinChen1999@users.noreply.github.com>
Date: Sat, 4 May 2024 23:06:45 +0200
Subject: [PATCH] Update documentation.md

---
 docs/src/documentation.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/docs/src/documentation.md b/docs/src/documentation.md
index bec12da..786064e 100644
--- a/docs/src/documentation.md
+++ b/docs/src/documentation.md
@@ -300,6 +300,10 @@ Additional parameters:
 params = [A] 
 ```
 
+
+<p align="justify"> Then we use nlsolve function to find the numerical solutions of our non-linear system of equations. Given the market-clearing conditions calculated from LR_main_eval() and specified set of varaibles we are interested in, the algorithms uses numerical methods to approximate the roots of multiple equations simultaneously. We can derive the optimal distribution of lands and labor from the optimization results. </p>
+
+
 ```julia
 result = nlsolve((res, x) -> res .= LR_main_eval(x, params), guess, show_trace=true, xtol=1e-16)
 ```
@@ -403,6 +407,8 @@ Additional parameters:
 params = [A] 
 ```
 
+<p align="justify"> Then we use nlsolve function to find the numerical solutions of our non-linear system of equations. Given the market-clearing conditions calculated from LR_market_eval() and specified set of varaibles we are interested in, the algorithms uses numerical methods to approximate the roots of multiple equations simultaneously. We can derive the optimal distribution of lands and labor from the optimization results. </p>
+
 ```julia
 result = nlsolve(x -> LR_market_eval(x, A), guess) 
 ```