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Introduction to optimization is a course that is made to teach students about discrete optimization. It includes research and computational complexity theory that helps students in knowing a lot about optimization. However, if you are pursuing CS 286P Introduction to Optimization course online, there is a chance that you might be looking for an online assignment help service provider to accomplish your course with flying grades. AllAssignmentHelp.com is that place that can help you in a professional manner throughout the course. You just need to ask us to take my Introduction to Optimization CS 286P class. Meanwhile, if you don’t know what the course consists of or are not familiar with some of its important points, then read till the end and know everything related to the course. Also, if you need expert’s assistance then our professionals are always there to make things easier for you. 

CS 286P Introduction to Optimization - Course Overview

The objective of this version of the course will be Discrete (Combinatorial) Optimization. Lets know more about the course.

Combinatorial optimization is an  area of mathematical optimization that is related to operations research and computational complexity.  

Combinatorial optimization is the process of selecting the best object or combination of objects from a limited number of options. Exhaustive search is not tractable in many of these issues because the set is finite yet cannot be identified. The travelling salesman issue, the minimum spanning tree problem, and the knapsack problem are examples of common problems. Airline operations, supply chain management, logistics, finance, healthcare, and environmental science and engineering are just a few examples of real-world applications.

Starting before WWII (the official dates seem to be 1939-1945), discrete optimization was the most powerful topic in the broader discipline of Operations Research. Ballistics (in general) was the initial impetus. 

Many tech firms have extensive optimization and operations research departments (Amazon, Uber, Didi). The others have smaller ones (Apple, Dell, Intel, and so on), which may or may not be related to supply chain management.

Grading Criteria For The CS 286P Course

Four individual homework assignments (40 percent of the grade) and five group projects (50 percent of the grade) will be used to determine the final grade (40 percent for the first 4 and 20 percent for the final project).

There will be no exam at the end of the course. The final assignment will be due at the planned final test on December 10th.

Assignments Evaluation

Group homework assignments will be completed in groups of 2-4 students, with the majority of groups consisting of three students. However, in this course, you need to form your own groups or if you're having problems finding a group, ask the TAs or Professor Regan for assistance. 

Moreover, latex should be used for written assignments (most students will use Overleaf). However, if you feel the need to pay someone to do my assignment in latex form, them AllAssignmentHelp.com's expert are here you to assist you with this. Our experts are well-equipped with this writing. 

Note: It is important to remember that the last individual homework assignment as well as the final project must be completed on time. But, in some cases, late homework assignments are acceptable but it should not take more than 3 days. 

You can hire our assignment homework writing experts if you are running a short of time. They will give you complete your course assignments and homework within the deadline. Also, you can request them to take my ntroduction to Optimization CS 286P class as well due to any issue in time. 

References and Readings Associated With CS 286P Introduction to Optimization Course

Here is a complete list of the references and the readings that you have to go through while pursuing the course. 

• Combinatorial Optimization: Theory and Algorithms, by Bernhard Korte and Jens Vygen. This can be found at the UCI library. 
• A Tutorial on Integer Programming, Gerard Cornuejols, Michael J. Trick, Matthew J. Saltzman, widely available on-line and also in the course files.
• Optimization by Grasp by M.G.C. Resend and C.C. Riberio – this is available online from the UCI library.
• Morrison, D.R., Jacobson, S.H., Sauppe, J.J. and Sewell, E.C., 2016. Branch-and-bound algorithms: A survey of recent advances in searching, branching, and pruning. Discrete Optimization, 19, pp.79-102.

Guidelines for Coding and Optimization Solvers

Homework assignments should be done in Python or Matlab with Gurobi. In case you are weak in python or matlab, you can take assignment writing help with both of them from our experts. They will assist you in the best possible manner and helped you in gaining some extra knowledge as well. 

This is the web link below where you need to register youself o the Gurobi for the work done. 

https://www.gurobi.com/downloads/end-user-license-agreement-academic/

Course Schedule

Here is the week wise schedue of the course along with topics and the chapters that you need to study. You can ask us to take my online class as well if you are busy or occupied with some other work. 

Week 1: Introduction to Optimization as a field, Introduction to current business application	Reading: Chapters 1 and 2 of Optimization by GRASP. Homework: Take the survey on Monday of week 1 if you can, but no later than before class on Wednesday. Install both the Cplex and Gurobi solvers, or investigate both and install the one you want to start with.
Week 2: Introduction to Classical Problems in Discrete Optimization	Knapsack problem Cutting stock problem Traveling salesman problem Vehicle routing problem Introduction to matrix formulation of problems Reading: A Tutorial on Integer Programming, Gerard Cornuejols, Michael J. Trick, Matthew J. Saltzman – you can focus on the problems listed above. Reading: What Healthcare is learning from Transportation and Manufacturing https://hcs.us.com/workflow-control/ Homework (individual): A set of problems will be provided. Write the mathematical formulations with clean descriptions and notatio
Week 3: Tricks for formulating Integer Programming Problems	Reading: TBD Homework: TBD
Week 4	Formulating Some Real Problems + Branch and Bound Monday – Case study in class – Ecotricity Wednesday – Branch and Bound Homework – Solve the ecotricity problem using Gurobi Homework – other formulations to be added after class on Wednesday (short additional assignment). Reading – Look over the Mixed integer programming formulations for single machine scheduling problems, by Keha, Khowala and Fowler – no need to read every word – the idea is to see how a problem can be formulated many different ways. Reading – The chapter on duality
Week 5	More about solving integer linear programs via Branch and Bound Guest Lectures  Branch and Bound with an LP Solver Heuristic Approach using greedy (depth first) Basic Simplex Algorithm Homework (Week 4): Foundations and Formulations Homework (Group Project): Implement a Branch and Bound solver in MATLAB or Python and integrate with the Gurobi or Cplex Solver. Write this code in a clean manner with comments. The problems will be announced on Monday of Week 5.
Week 6: Extended vs Compact Formulations	Guest Lecture Compact formulation of the cutting stock problem – see where this goes wrong Expanded form of the cutting stock problem. Branching only helps a little in the compact formulation How many columns are needed? Examine cases where the expanded formulation is weak. Homework (Group Project): Code the weak LP relaxation and code the strong LP relaxation. Fully enumerate all columns for strong relaxation. Note how many branches are required for the weak formulation. Also note how many columns are   required for the strong formulation as problem size grows.
Week 7: Column Generation: Basic	Guest Lecture Primal and Dual formulation of a linear program. Column generation Pricing using a dynamic program. The Lagrangian bound. Rounding the solution to the ILP solver on a subset of the columns Homework (Group Project): Implement column generation on bin packing problem. Use different LP solvers and note the difference in convergence.
Week 8: Column Generation: Dual Stabilization	Guest Lecture  Degeneracy Classic dual optimal inequalities for cutting stock problems Box step method. Various smoothing methods Homework (Group Project): Implement the box step method, or a smoothing method for the single source capacitated facility location problem.
Weeks 9 and 10: Metaheuristics Revisited	Genetic Algorithms, Tabu Search, GRASP (revisited), Harmony Search Reading: TBD Final Project (Group Project) – select an optimization problem from a set that will be provided – solve this problem with the metaheuristic of your choice. Submit Your Final Project by the end of the Day on December 10th.

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