Guías Docentes Electrónicas
1. General information
Course:
NONLINEAR ANALYSIS AND DIFFERENTIAL EQUATIONS
Code:
310938
Type:
ELECTIVE
ECTS credits:
6
Degree:
2351 - MASTER DEGREE PROGRAMME IN PHYSICS AND MATHEMATICS-FISYMAT
Academic year:
2021-22
Center:
602 - E.T.S. INDUSTRIAL ENGINEERING OF C. REAL
Group(s):
20 
Year:
1
Duration:
C2
Main language:
Spanish
Second language:
English
Use of additional languages:
English Friendly:
Y
Web site:
Bilingual:
N
Lecturer: JOSE CARLOS BELLIDO GUERRERO - Group(s): 20 
Building/Office
Department
Phone number
Email
Office hours
2-A22
MATEMÁTICAS
926295261
josecarlos.bellido@uclm.es

Lecturer: ALBERTO DONOSO BELLON - Group(s): 20 
Building/Office
Department
Phone number
Email
Office hours
Edificio Politécnico/2-B17
MATEMÁTICAS
926295251
alberto.donoso@uclm.es

2. Pre-Requisites

Previous knowledge of multivariable calculus, linear algebra, and ordinary and partial differential equations is required

3. Justification in the curriculum, relation to other subjects and to the profession

This course pretends to be a first contact to the field of optimization through mathamatical programming, calculus of variations and optimal control. It will be of great help not only for students with mathematical background but also for physicians and engineers interested in modeling some problems as optimization ones.


4. Degree competences achieved in this course
Course competences
Code Description
CB06 Possess and understand knowledge that provides a basis or opportunity to be original in the development and / or application of ideas, often in a research context.
CB07 Apply the achieved knowledge and ability to solve problems in new or unfamiliar environments within broader (or multidisciplinary) contexts related to the area of study
CB08 Be able to integrate knowledge and face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on social and ethical responsibilities linked to the application of knowledge and judgments
CB09 Know how to communicate the conclusions and their supported knowledge and ultimate reasons to specialized and non-specialized audiences in a clear and unambiguous way
CB10 Have the learning skills which allow to continue studying in a self-directed or autonomous way
CE03 Have the ability to build and develop advanced mathematical reasoning, and delve into the different fields of mathematics
CG05 Gain the ability to develop a scientific research work independently and in its entirety. Be able to search and assimilate scientific literature, formulate hypotheses, raise and develop problems and draw conclusions from the obtained results
CT03 Develop critical reasoning and the ability to criticize and self-criticize
5. Objectives or Learning Outcomes
Course learning outcomes
Description
Become familiar with the different techniques of Nonlinear Analysis
Be able to apply the acquired knowledge to treat different non-linear differential equations
To conceive the need for weak derivation in the environment of Sobolev spaces
Additional outcomes
Not established.
6. Units / Contents
  • Unit 1: Linear programming
  • Unit 2: Non-linear programming
  • Unit 3: Calculus of Variations
  • Unit 4: Optimal control
  • Unit 5: Variational methods for non-linear analysis
7. Activities, Units/Modules and Methodology
Training Activity Methodology Related Competences (only degrees before RD 822/2021) ECTS Hours As Com Description
Class Attendance (theory) [ON-SITE] Lectures 1.2 30 N N
Class Attendance (practical) [ON-SITE] Problem solving and exercises 0.6 15 N N
Writing of reports or projects [OFF-SITE] Self-study 1.2 30 N N
Study and Exam Preparation [OFF-SITE] Self-study 2.4 60 N N
Project or Topic Presentations [ON-SITE] Assessment tests 0.04 1 Y Y
Individual tutoring sessions [ON-SITE] Guided or supervised work 0.52 13 N N
Final test [ON-SITE] Assessment tests 0.04 1 Y Y
Total: 6 150
Total credits of in-class work: 2.4 Total class time hours: 60
Total credits of out of class work: 3.6 Total hours of out of class work: 90

As: Assessable training activity
Com: Training activity of compulsory overcoming (It will be essential to overcome both continuous and non-continuous assessment).

8. Evaluation criteria and Grading System
Evaluation System Continuous assessment Non-continuous evaluation * Description
Final test 15.00% 30.00% Regular tests
Assessment of problem solving and/or case studies 15.00% 0.00% Exercises to support the main concepts
Projects 70.00% 70.00% Oral presentation of a case study
Total: 100.00% 100.00%  
According to art. 4 of the UCLM Student Evaluation Regulations, it must be provided to students who cannot regularly attend face-to-face training activities the passing of the subject, having the right (art. 12.2) to be globally graded, in 2 annual calls per subject , an ordinary and an extraordinary one (evaluating 100% of the competences).

Evaluation criteria for the final exam:
  • Continuous assessment:
    Evaluation criteria not defined
  • Non-continuous evaluation:
    Evaluation criteria not defined

Specifications for the resit/retake exam:
Evaluation criteria not defined
Specifications for the second resit / retake exam:
Evaluation criteria not defined
9. Assignments, course calendar and important dates
Not related to the syllabus/contents
Hours hours
Writing of reports or projects [AUTÓNOMA][Self-study] 75

Unit 1 (de 5): Linear programming
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 15

Unit 2 (de 5): Non-linear programming
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 15

Unit 3 (de 5): Calculus of Variations
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 15

Unit 4 (de 5): Optimal control
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 15

Unit 5 (de 5): Variational methods for non-linear analysis
Activities Hours
Class Attendance (theory) [PRESENCIAL][Lectures] 15

Global activity
Activities hours
10. Bibliography and Sources
Author(s) Title Book/Journal Citv Publishing house ISBN Year Description Link Catálogo biblioteca
Pablo Pedregal Introduction to Optimization Springer 0-387-40398-1 2004 Ficha de la biblioteca



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