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
دکتر رضا قنبری
دانشیار
دکتر رضا قنبری
دانشیار
دانشکده علوم ریاضی
ریاضی کاربردی
سرفصل مطالب
# سرفصل مطالب طرح درس (براساس سرفصل)
1 معرفی الگوریتم
2 تعریف پیچیدگی
3 بررسی ویژگی الگوریتم خوب
4 تفاوت الگوریتم با برنامه نویسی
5 تفاوت مساله با نمونه
6 انواع پیچیدگی محاسباتی
7 چند نمونه الگوریتم
8 بیان پیچیدگی محاسباتی نمونه الگوریتم های بیان شده
9 سختی مساله چیست
10 معرفی مسایل NP سخت
11 معرفی چند ساختار داده، صف
12 معرفی چند ساختار داده، پشته
13 برنامه نوسی شی گرا
14 بررسی پیچیدگی الگوریتم DFS
15 بررسی مسایل مهم فصل 22
16 معرفی الکوریتم DFS
17 بررسی مسایل مهم فصل 22
18 بیان برخی مدلسازی های مهم
19 معرفی الگوریتم های پایه ای (الگوریتم پریم)
20 معرفی الگوریتم های پایه ای (الگوریتم کروسکال)
21 معرفی مساله بیشترین جریان
22 معرفی الگوریتم های پایه ای (جانسون)
23 معرفی الگوریتم های پایه ای (فلوید وارشال)
24 معرفی الگوریتم های پایه ای (بلمن فورد)
25 معرفی الگوریتم های پایه ای (دایجسترا)
26 آشنایی با نرم افزار OPL
27 پیاده سازی مسایل مهم در OPL
28 آشنایی با نرم افزار پایتون
29 آشنایی با کتابخانه Pyomo
30 پیاده سازی در Pyomo