IE 303 • Final • Modeling and Methods in Optimization
Integer Programming ve türevlerine dair oldukça ayrıntılı konu anlatımları ve çözümlü örneklerle hazırlanmış bu dersle birlikte IE 303 gibi zor bir dersi bile kolay hale getir.
Eğitmen
Ömer Faruk Altun
Co-founder & Head of Education
2011 yılında Endüstri Mühendisliği okumak için başladığım Sabancı Üniversitesi'nden 2018 yılında Bilgisayar Mühendisi olarak mezun oldum. 13 yıldır Altun ismiyle başta Sabancı Üniversitesi olmak üzere çeşitli okullarda Endüstri ve Bilgisayar Mühendisliği alanlarında ders vermekteyim. Unicourse'ta sunduğum derslerin yanında eğitim departmanının da sorumluluğunu üstlenmekteyim.
Paketi Tamamla
🎓 Bilkent Üniversitesinde öğrencilerin %92'si tüm paketi alarak çalışıyor.
IE 303 • Midterm
Modeling and Methods in Optimization
Ömer Faruk Altun
1799 TL
IE 303 • Final
Modeling and Methods in Optimization
Ömer Faruk Altun
1799 TL
Konular
Spanning Trees
Graphs and Their Spanning Trees
Example 1
Maximum Weight Spanning Trees
Formulation I
Example 2
Formulation II
Example 3
Greedy Heuristic
Example 4
Minimum Weight Spanning Trees
Example 5
Greedy Heuristic
Example 6
Matching and Covering Problems
Matching
Maximum Cardinality Matching Problem
Example 1
Bipartite Graphs
Minimum Cardinality Covering Problem
Example 2
Traveling Salesman Problem
Hamiltonian Tours and TSP
2-matching
STSP: 2-matching with Subtour Elimination
Example 1
STSP: 2-matching with Connectivity
Example 2
1-Tree
Example 3
STSP: 2-matching & 1-Tree
Example 4
Finding Optimal 1-Tree
Example 5
Asymmetric Traveling Salesman Problem
ATSP Formulations I & II
Miller-Tucker-Zemlin Formulation
Example 6
Exam Practice - II
Spanning Trees 1
Spanning Trees 2
Matching and Covering Problems 1
Matching and Covering Problems 2
Matching and Covering Problems 3
Matching and Covering Problems 4
Traveling Salesman Problem 1
Traveling Salesman Problem 2
Dynamic Programming
A New Approach to Optimization Problems
Inventory Problems
Example 1
Resource Allocation Problems
Example 2
Non-additive (Probabilistic) Dynamic Programming
What is the difference?
Example 1
Example 2
Example 3
Heuristic Methods
P v NP
Constructive Heuristics
Improvement Heuristics
Metaheuristics
Simulated Annealing
Genetic Algorithm
Tabu Search
Exam Practice - III
Deterministic Dynamic Programming 1
Deterministic Dynamic Programming 2
Deterministic Dynamic Programming 3
Deterministic Dynamic Programming 4
Deterministic Dynamic Programming 5
Deterministic Dynamic Programming 6
Deterministic Dynamic Programming 7
Deterministic Dynamic Programming 8
Deterministic Dynamic Programming 9
Deterministic Dynamic Programming 10
Deterministic Dynamic Programming 11
Deterministic Dynamic Programming 12
Deterministic Dynamic Programming 13
Deterministic Dynamic Programming 14
Probabilistic Dynamic Programming 1
Probabilistic Dynamic Programming 2
Probabilistic Dynamic Programming 3
Probabilistic Dynamic Programming 4
Probabilistic Dynamic Programming 5
Heuristics 1
Heuristics 2
Heuristics 3
Heuristics 4
Heuristics 5
Heuristics 6
Heuristics 7
Heuristics 8
Heuristics 9
Değerlendirmeler
10 numara
Ders İçeriği
Spanning Trees
Graphs and Their Spanning Trees
Example 1
Maximum Weight Spanning Trees
Formulation I
Example 2
Formulation II
Example 3
Greedy Heuristic
Example 4
Minimum Weight Spanning Trees
Example 5
Greedy Heuristic
Example 6
Matching and Covering Problems
Matching
Maximum Cardinality Matching Problem
Example 1
Bipartite Graphs
Minimum Cardinality Covering Problem
Example 2
Traveling Salesman Problem
Hamiltonian Tours and TSP
2-matching
STSP: 2-matching with Subtour Elimination
Example 1
STSP: 2-matching with Connectivity
Example 2
1-Tree
Example 3
STSP: 2-matching & 1-Tree
Example 4
Finding Optimal 1-Tree
Example 5
Asymmetric Traveling Salesman Problem
ATSP Formulations I & II
Miller-Tucker-Zemlin Formulation
Example 6
Exam Practice - II
Spanning Trees 1
Spanning Trees 2
Matching and Covering Problems 1
Matching and Covering Problems 2
Matching and Covering Problems 3
Matching and Covering Problems 4
Traveling Salesman Problem 1
Traveling Salesman Problem 2
Dynamic Programming
A New Approach to Optimization Problems
Inventory Problems
Example 1
Resource Allocation Problems
Example 2
Non-additive (Probabilistic) Dynamic Programming
What is the difference?
Example 1
Example 2
Example 3
Heuristic Methods
P v NP
Constructive Heuristics
Improvement Heuristics
Metaheuristics
Simulated Annealing
Genetic Algorithm
Tabu Search
Exam Practice - III
Deterministic Dynamic Programming 1
Deterministic Dynamic Programming 2
Deterministic Dynamic Programming 3
Deterministic Dynamic Programming 4
Deterministic Dynamic Programming 5
Deterministic Dynamic Programming 6
Deterministic Dynamic Programming 7
Deterministic Dynamic Programming 8
Deterministic Dynamic Programming 9
Deterministic Dynamic Programming 10
Deterministic Dynamic Programming 11
Deterministic Dynamic Programming 12
Deterministic Dynamic Programming 13
Deterministic Dynamic Programming 14
Probabilistic Dynamic Programming 1
Probabilistic Dynamic Programming 2
Probabilistic Dynamic Programming 3
Probabilistic Dynamic Programming 4
Probabilistic Dynamic Programming 5
Heuristics 1
Heuristics 2
Heuristics 3
Heuristics 4
Heuristics 5
Heuristics 6
Heuristics 7
Heuristics 8
Heuristics 9
Sıkça Sorulan Sorular
Örneğin, Koç Üniversitesi - MATH 101 (Calculus) veya başka bir okulun benzer dersi olsun, paketlerimiz tam da o derse göre tasarlanır. Böylece nokta atışı çalışır, zaman kazanırsın.
Sınava özel videolar —konu anlatımları, çıkmış sorular ve çözümleri, özet notlar—içerir. Sınavda sıkça çıkan soruları hedefler. Eğitmenlerimiz, üniversitenin akademik takvimini takip ederek paketleri sürekli günceller. Böylece, gereksiz detaylarla vakit kaybetmeden başarını artırmaya odaklanabilirsin.