最適化ソフトウェアに関する研究で著名な Hans D. Mittelmann 氏とStefan Vigerske 氏を招いて
来年(2023年)の1月16日(月)～20日(金)に集中講義（最適化アルゴリズムとソフトウェアに関する講義と演習）を行うことになりました。
内容は以下の授業内容を参照下さい。第一線の専門家から最先端の最適化ソフトウェアに関する内容を学ぶことが出来ます。
授業内容（予定）
Session 1: Introduction, What will be covered, which resources will be used
Which software is available for unconstrained optimization?
Exercise: Use AMPL to solve unconstrained problems with unique
and multiple solutions
2023/01/16(Mon): 13:00 ? 14:30 [Lecture for Session 1]
2023/01/16(Mon): 14:50～16:20 [Exercise for Session 1]
Session 2: Nonlinear Least Squares and Nonlinear Systems of Equations
Which methods are available and have been implemented?
Exercise: Regular and singular nonlinear systems, a challenging
least squares problem also solved via orthogonal distance regression
2023/01/17(Tue): 10:30～12:00 [Answer for Session 1 and Lecture for Session 2]
2023/01/17(Tue): 13:30～14:30 [Exercise for Session 2]
Session 3: Constrained Nonlinear Programming, NLP
Classical methods such as SQP; interior point methods and
available software
Exercise: A bilevel NLP, a problem from distance geometry and one
from financial math
2023/01/17(Tue): 14:50～16:20 [Answer for Session 2 and lecture for Session 3]
2023/01/18(Wed): 10:30～12:00 [Exercise for Session 3]
2023/01/18(Wed): 13:30～14:30 [Answer for Session 3 and a kind of summary so far]
Session 4: Convex (and some nonconvex) Optimization, LP, QP, SDP, SOCP
Exercise: An LP from Compressive Sensing, two problems from Machine
Learning, an SOCP problem from Robust Optimization, using CPLEX,
Gurobi, and a global solver on nonconvex problems
2023/01/18(Wed): 14:50～16:20 [Lecture for Session 4]
2023/01/19(Thu): 10:30～12:00 [Answer for Session 4 and starts lecture for Session 5]
Session 5: Mixed Integer Linear Programming
Algorithms and available software
Exercise: Quadratic Assignment Problem
2023/01/19(Thu): 13:30～14:30 [Lectrue for Session 5]
2023/01/19(Thu): 14:50～16:20 [Exersise for Session 5]
2023/01/19(Thu): 16:50～17:50 [Answer for Session 5 and Summary]
Session 6: Mixed Integer Nonlinear Programming (MINLP), Global Optimization
Methods for Convex MINLP, e.g. Outer Approximation, branch-and-bound
Methods for Deterministic Global Optimization (nonconvex (MI)NLP), e.g., convexification, spatial branch-and-bound
Available Software
Exercise: a MINLP
2023/01/20(Fri): 10:30～12:00
2023/01/20(Fri): 13:30～15:00
最適化ソフトウェアに関する研究で著名な Hans D. Mittelmann 氏とStefan Vigerske 氏を招いて
来年(2023年)の1月16日(月)～20日(金)に集中講義（最適化アルゴリズムとソフトウェアに関する講義と演習）を行うことになりました。
内容は以下の授業内容を参照下さい。第一線の専門家から最先端の最適化ソフトウェアに関する内容を学ぶことが出来ます。
授業内容（予定）
Session 1: Introduction, What will be covered, which resources will be used
Which software is available for unconstrained optimization?
Exercise: Use AMPL to solve unconstrained problems with unique
and multiple solutions
2023/01/16(Mon): 13:00 ? 14:30 [Lecture for Session 1]
2023/01/16(Mon): 14:50～16:20 [Exercise for Session 1]
Session 2: Nonlinear Least Squares and Nonlinear Systems of Equations
Which methods are available and have been implemented?
Exercise: Regular and singular nonlinear systems, a challenging
least squares problem also solved via orthogonal distance regression
2023/01/17(Tue): 10:30～12:00 [Answer for Session 1 and Lecture for Session 2]
2023/01/17(Tue): 13:30～14:30 [Exercise for Session 2]
Session 3: Constrained Nonlinear Programming, NLP
Classical methods such as SQP; interior point methods and
available software
Exercise: A bilevel NLP, a problem from distance geometry and one
from financial math
2023/01/17(Tue): 14:50～16:20 [Answer for Session 2 and lecture for Session 3]
2023/01/18(Wed): 10:30～12:00 [Exercise for Session 3]
2023/01/18(Wed): 13:30～14:30 [Answer for Session 3 and a kind of summary so far]
Session 4: Convex (and some nonconvex) Optimization, LP, QP, SDP, SOCP
Exercise: An LP from Compressive Sensing, two problems from Machine
Learning, an SOCP problem from Robust Optimization, using CPLEX,
Gurobi, and a global solver on nonconvex problems
2023/01/18(Wed): 14:50～16:20 [Lecture for Session 4]
2023/01/19(Thu): 10:30～12:00 [Answer for Session 4 and starts lecture for Session 5]
Session 5: Mixed Integer Linear Programming
Algorithms and available software
Exercise: Quadratic Assignment Problem
2023/01/19(Thu): 13:30～14:30 [Lectrue for Session 5]
2023/01/19(Thu): 14:50～16:20 [Exersise for Session 5]
2023/01/19(Thu): 16:50～17:50 [Answer for Session 5 and Summary]
Session 6: Mixed Integer Nonlinear Programming (MINLP), Global Optimization
Methods for Convex MINLP, e.g. Outer Approximation, branch-and-bound
Methods for Deterministic Global Optimization (nonconvex (MI)NLP), e.g., convexification, spatial branch-and-bound
Available Software
Exercise: a MINLP
2023/01/20(Fri): 10:30～12:00
2023/01/20(Fri): 13:30～15:00
最適化ソフトウェアに関する研究で著名な Hans D. Mittelmann 氏とStefan Vigerske 氏を招いて
来年(2023年)の1月16日(月)～20日(金)に集中講義（最適化アルゴリズムとソフトウェアに関する講義と演習）を行うことになりました。
内容は以下の授業内容を参照下さい。第一線の専門家から最先端の最適化ソフトウェアに関する内容を学ぶことが出来ます。
授業内容（予定）
Session 1: Introduction, What will be covered, which resources will be used
Which software is available for unconstrained optimization?
Exercise: Use AMPL to solve unconstrained problems with unique
and multiple solutions
2023/01/16(Mon): 13:00 ? 14:30 [Lecture for Session 1]
2023/01/16(Mon): 14:50～16:20 [Exercise for Session 1]
Session 2: Nonlinear Least Squares and Nonlinear Systems of Equations
Which methods are available and have been implemented?
Exercise: Regular and singular nonlinear systems, a challenging
least squares problem also solved via orthogonal distance regression
2023/01/17(Tue): 10:30～12:00 [Answer for Session 1 and Lecture for Session 2]
2023/01/17(Tue): 13:30～14:30 [Exercise for Session 2]
Session 3: Constrained Nonlinear Programming, NLP
Classical methods such as SQP; interior point methods and
available software
Exercise: A bilevel NLP, a problem from distance geometry and one
from financial math
2023/01/17(Tue): 14:50～16:20 [Answer for Session 2 and lecture for Session 3]
2023/01/18(Wed): 10:30～12:00 [Exercise for Session 3]
2023/01/18(Wed): 13:30～14:30 [Answer for Session 3 and a kind of summary so far]
Session 4: Convex (and some nonconvex) Optimization, LP, QP, SDP, SOCP
Exercise: An LP from Compressive Sensing, two problems from Machine
Learning, an SOCP problem from Robust Optimization, using CPLEX,
Gurobi, and a global solver on nonconvex problems
2023/01/18(Wed): 14:50～16:20 [Lecture for Session 4]
2023/01/19(Thu): 10:30～12:00 [Answer for Session 4 and starts lecture for Session 5]
Session 5: Mixed Integer Linear Programming
Algorithms and available software
Exercise: Quadratic Assignment Problem
2023/01/19(Thu): 13:30～14:30 [Lectrue for Session 5]
2023/01/19(Thu): 14:50～16:20 [Exersise for Session 5]
2023/01/19(Thu): 16:50～17:50 [Answer for Session 5 and Summary]
Session 6: Mixed Integer Nonlinear Programming (MINLP), Global Optimization
Methods for Convex MINLP, e.g. Outer Approximation, branch-and-bound
Methods for Deterministic Global Optimization (nonconvex (MI)NLP), e.g., convexification, spatial branch-and-bound
Available Software
Exercise: a MINLP
2023/01/20(Fri): 10:30～12:00
2023/01/20(Fri): 13:30～15:00
最適化ソフトウェアに関する研究で著名な Hans D. Mittelmann 氏とStefan Vigerske 氏を招いて
来年(2023年)の1月16日(月)～20日(金)に集中講義（最適化アルゴリズムとソフトウェアに関する講義と演習）を行うことになりました。
内容は以下の授業内容を参照下さい。第一線の専門家から最先端の最適化ソフトウェアに関する内容を学ぶことが出来ます。
授業内容（予定）
Session 1: Introduction, What will be covered, which resources will be used
Which software is available for unconstrained optimization?
Exercise: Use AMPL to solve unconstrained problems with unique
and multiple solutions
2023/01/16(Mon): 13:00 ? 14:30 [Lecture for Session 1]
2023/01/16(Mon): 14:50～16:20 [Exercise for Session 1]
Session 2: Nonlinear Least Squares and Nonlinear Systems of Equations
Which methods are available and have been implemented?
Exercise: Regular and singular nonlinear systems, a challenging
least squares problem also solved via orthogonal distance regression
2023/01/17(Tue): 10:30～12:00 [Answer for Session 1 and Lecture for Session 2]
2023/01/17(Tue): 13:30～14:30 [Exercise for Session 2]
Session 3: Constrained Nonlinear Programming, NLP
Classical methods such as SQP; interior point methods and
available software
Exercise: A bilevel NLP, a problem from distance geometry and one
from financial math
2023/01/17(Tue): 14:50～16:20 [Answer for Session 2 and lecture for Session 3]
2023/01/18(Wed): 10:30～12:00 [Exercise for Session 3]
2023/01/18(Wed): 13:30～14:30 [Answer for Session 3 and a kind of summary so far]
Session 4: Convex (and some nonconvex) Optimization, LP, QP, SDP, SOCP
Exercise: An LP from Compressive Sensing, two problems from Machine
Learning, an SOCP problem from Robust Optimization, using CPLEX,
Gurobi, and a global solver on nonconvex problems
2023/01/18(Wed): 14:50～16:20 [Lecture for Session 4]
2023/01/19(Thu): 10:30～12:00 [Answer for Session 4 and starts lecture for Session 5]
Session 5: Mixed Integer Linear Programming
Algorithms and available software
Exercise: Quadratic Assignment Problem
2023/01/19(Thu): 13:30～14:30 [Lectrue for Session 5]
2023/01/19(Thu): 14:50～16:20 [Exersise for Session 5]
2023/01/19(Thu): 16:50～17:50 [Answer for Session 5 and Summary]
Session 6: Mixed Integer Nonlinear Programming (MINLP), Global Optimization
Methods for Convex MINLP, e.g. Outer Approximation, branch-and-bound
Methods for Deterministic Global Optimization (nonconvex (MI)NLP), e.g., convexification, spatial branch-and-bound
Available Software
Exercise: a MINLP
2023/01/20(Fri): 10:30～12:00
2023/01/20(Fri): 13:30～15:00
最適化ソフトウェアに関する研究で著名な Hans D. Mittelmann 氏とStefan Vigerske 氏を招いて
来年(2023年)の1月16日(月)～20日(金)に集中講義（最適化アルゴリズムとソフトウェアに関する講義と演習）を行うことになりました。
内容は以下の授業内容を参照下さい。第一線の専門家から最先端の最適化ソフトウェアに関する内容を学ぶことが出来ます。
授業内容（予定）
Session 1: Introduction, What will be covered, which resources will be used
Which software is available for unconstrained optimization?
Exercise: Use AMPL to solve unconstrained problems with unique
and multiple solutions
2023/01/16(Mon): 13:00 ? 14:30 [Lecture for Session 1]
2023/01/16(Mon): 14:50～16:20 [Exercise for Session 1]
Session 2: Nonlinear Least Squares and Nonlinear Systems of Equations
Which methods are available and have been implemented?
Exercise: Regular and singular nonlinear systems, a challenging
least squares problem also solved via orthogonal distance regression
2023/01/17(Tue): 10:30～12:00 [Answer for Session 1 and Lecture for Session 2]
2023/01/17(Tue): 13:30～14:30 [Exercise for Session 2]
Session 3: Constrained Nonlinear Programming, NLP
Classical methods such as SQP; interior point methods and
available software
Exercise: A bilevel NLP, a problem from distance geometry and one
from financial math
2023/01/17(Tue): 14:50～16:20 [Answer for Session 2 and lecture for Session 3]
2023/01/18(Wed): 10:30～12:00 [Exercise for Session 3]
2023/01/18(Wed): 13:30～14:30 [Answer for Session 3 and a kind of summary so far]
Session 4: Convex (and some nonconvex) Optimization, LP, QP, SDP, SOCP
Exercise: An LP from Compressive Sensing, two problems from Machine
Learning, an SOCP problem from Robust Optimization, using CPLEX,
Gurobi, and a global solver on nonconvex problems
2023/01/18(Wed): 14:50～16:20 [Lecture for Session 4]
2023/01/19(Thu): 10:30～12:00 [Answer for Session 4 and starts lecture for Session 5]
Session 5: Mixed Integer Linear Programming
Algorithms and available software
Exercise: Quadratic Assignment Problem
2023/01/19(Thu): 13:30～14:30 [Lectrue for Session 5]
2023/01/19(Thu): 14:50～16:20 [Exersise for Session 5]
2023/01/19(Thu): 16:50～17:50 [Answer for Session 5 and Summary]
Session 6: Mixed Integer Nonlinear Programming (MINLP), Global Optimization
Methods for Convex MINLP, e.g. Outer Approximation, branch-and-bound
Methods for Deterministic Global Optimization (nonconvex (MI)NLP), e.g., convexification, spatial branch-and-bound
Available Software
Exercise: a MINLP
2023/01/20(Fri): 10:30～12:00
2023/01/20(Fri): 13:30～15:00
