# LECTURE: CLASSICAL OPTIMIZATION OVERVIEW

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Florida International UniversityDepartment of Civil and Environmental EngineeringOptimization in Water Resources Engineering, Spring 2020LECTURE: CLASSICAL OPTIMIZATION OVERVIEWArturo S. Leon, Ph.D., P.E., D.WRE

Optimization problem Design variables: variables with which the design problem isparameterized:Objective: quantity that is to be minimized (maximized)Usually denoted by:( ŌĆ£cost functionŌĆØ)Constraint: condition that has to be satisfied Inequality constraint: Equality constraint:

Solving optimization problems Optimization problems are typically solved using an iterative sponsesDerivatives ofresponses(design sensitivities)

Defining an optimization problem1.Choose design variables and their bounds2.Formulate objective3.Formulate constraints (restrictions)4.Choose suitable optimization algorithm

Example ŌĆō Design of a SODA Can5 Design a SODA can to hold an specifiedamount of SODA and other requirements.The cans will be produced in billions, so itis desirable to minimize the cost ofmanufacturing.Since the cost is related directly to thesurface area of the sheet metal used, it isreasonable to minimize the sheet metalrequired to fabricate the can.

Example ŌĆō Design of a SODA Can (Cont.)6Requirements:1.The diameter of the can should be nomore than 8 cm and no less than 3.5cm.2.The height of the can should be nomore than18 cm and no less than 8 cm.3.The can is required to hold at least400 ml of fluid.

Example ŌĆō Design of a SODA Can (Cont.)7Design variablesD diameter of the can (cm)H height of the can (cm)Objective functionThe design objective is to minimize the surface area

Example ŌĆō Design of a SODA Can (Cont.)8The constraints must be formulated in terms of design variables.The first constraint is that the can must hold at least 400 ml of fluid.The other constraints on the size of the can are:The problem has two independent design variables and five explicitconstraints.

Optimization Problem CharacteristicsLinearity Nonlinear objective functions can have multiplelocal optima:fx2fx2x Challenge: finding the global optimum.x1x1

Unimodality Bracketing and sectioning methods work best for unimodal functions:ŌĆ£An unimodal function consists of exactly one monotonically increasing anddecreasing partŌĆØ

Convexity Convex set:ŌĆ£A set S is convex if for every two points x1, x2 in S, theconnecting line also lies completely inside SŌĆØ

Lagrange Multipliers12 The method of Lagrange multipliers gives a set ofnecessary conditions to identify optimal points ofequality constrained optimization problems.This is done by converting a constrained problem to anequivalent unconstrained problem with the help ofcertain unspecified parameters known as Lagrangemultipliers.

Finding an Optimum using Lagrange Multipliers13The classical problem formulationminimize f(x1, x2, ., xn)Subject to h1(x1, x2, ., xn) 0can be converted tominimize L(x, ) f(x) - h1(x)whereL(x, ) is the Lagrangian function is an unspecified positive or negative constant called theLagrangian Multiplier

Lagrange Multipliers Method141.Original problem is rewritten as:minimize L(x, ) f(x) - h1(x)2.Take derivatives of L(x, ) with respect to xi and set them equal to zero. If there are n variables (i.e., x1, ., xn) then you will get n equations with n 1unknowns (i.e., n variables xi and one Lagrangian multiplier )3.Express all xi in terms of Langrangian multiplier 4.Plug x in terms of in constraint h1(x) 0 and solve .5.Calculate x by using the just found value for . Note that the n derivatives and one constraint equation result in n 1 equationsfor n 1 variables!

Multiple constraints15 The Lagrangian multiplier method can be used for any number of equalityconstraints.Suppose we have a classical problem formulation with k equality constraintsminimizef(x1, x2, ., xn)Subject toh1(x1, x2, ., xn) 0.hk(x1, x2, ., xn) 0This can be converted inminimizeL(x, ) f(x) - T h(x)Where T is the transpose vector of Lagrangian multpliers and has length k

EXAMPLE16A factory manufactures HONDA CITY and HONDA CIVIC cars. Determine the optimalnumber of HONDA CITY and HONDA CIVIC cars produced if the factory capacity is90 cars per day, and the cost of manufacturing is C (x, y) 6x2 12y2, where x isthe number of HONDA CITY cars and y is the number of HONDA CIVIC carsproduced.

EXAMPLE (Cont.)17 VARIABLESx No. of HONDA CITY cars producedy No. of HONDA CIVIC cars produced COST of Manufacturing;C (x, y) 6x2 12y2OBJECTIVE:MINIMIZE COSTCONSTRAINT: 90 cars per dayx y 90Original problem is rewritten as:minimize L(x, ) f(x) - h1(x)

EXAMPLE (Cont.)18

Unconstrained optimization algorithms Single-variable methods 0th order (involving only f ) 1st order (involving f and f ŌĆÖ ) 2nd order (involving f, f ŌĆÖ and f ŌĆØ )Multiple variable methods Gradient Descent Methods Simplex Method Sequential Linear Programming Sequential Quadratic Programming Etc.

Single-variable methodsBisection method Optimality conditions: minimum at stationary point Root finding offŌĆÖ Similar to sectioning methods, but uses derivative:ffŌĆÖInterval is halved in each iteration. Note, this isbetter than any of the direct methods

NewtonŌĆÖs methodfŌĆÖ Again, root finding of Basis: Taylor approximation off ŌĆÖ:f '( x h ) f '( x ) f ''( x ) h o ( h 2 ) h New guess:f '(x)f "(x)x k 1 x k h k x k f '( xk )f " ( xk )Linearapproximation

NewtonŌĆÖs method (cont.) Best convergence of all methods:fŌĆÖfŌĆÖxkxk 2xk Unless it divergesxk 1xk 1xk 2

Summary single variable methods Bracketing Dichotomous sectioning Fibonacci sectioning Golden ratio sectioning Quadratic interpolation Cubic interpolation Bisection method Secant method Newton method And many, many more!0th order1st order2nd order

MATLAB DEMO: Single variable MinimizationThis demo will show a number of ways to minimize f(x) starting at multiple initial points.Demo Folder: Single variable Classical Optimization (Download file from Canvas)Demo File: Main File Single Variable.mFunction: xcos(2x)

Single variable Minimization (cont.)(1) Change starting points(2) Discuss and show sensitivity of solutions

Multiple variable methodsGRADIENT DESCENT METHODSJ ( x), x x1 , x2 ,.xn Consider a function The gradient of J(x) at a point x0 is a vector of length n. J 0 x ( x ) 1 J 0 x ( x ) 2 J ( x 0 ) . . . J ( x 0 ) xn Each element in the vector is evaluated at the point x0.

GRADIENT DESCENT METHODS (cont.) Linear Programming Simplex Method Newton-Raphson Method Secant Method Bisection Method Line Search Methods Sequential Linear Programming Sequential Quadratic Programming Karush-Kuhn-Tucker Conditions (KKT)

SIMPLEX METHOD Solutions at the ŌĆ£verticesŌĆØ of the designspace are called basic feasiblesolutions.The Simplex algorithm moves from BFS toBFS so that the objective alwaysimproves.

SEQUENTIAL LINEAR PROGRAMMING Consider a general nonlinear problem linearized via first order Taylor series:This is an LP problem with the design variables contained in ╬┤x. The functions andgradients evaluated at x0 are constant coefficients.

SEQUENTIAL LINEAR PROGRAMMING (Cont.)1.Initial guess x02.Linearize about x0 using first order Taylor series3.Solve resulting LP to find ╬┤x4.Update x1 x0 ╬┤x5.Linearize about x1 and repeat:xq xq-1 ╬┤xWhere ╬┤x is the solution of LP (model linearized about xq-1.

SEQUENTIAL QUADRATIC PROGRAMMING Create a quadratic approximation to the LagrangianCreate linear approximations to the constraintsSolve the quadratic problem to find the search direction, SPerform the 1-D searchUpdate the approximation to the Lagrangian

Newton methodExpand f(x) by its Taylor series about the point xkwhere the gradient is the vectorand the Hessian is the symmetric matrix

Newton method (Cont.)For a minimum we require thatwith solution, and so. This gives the iterative update If f(x) is quadratic, then the solution is found in one step. The method has quadratic convergence (as in the 1D case). The solution is guaranteed to be a downhill direction.Rather than jump straight to the minimum, it is better to perform a line minimization which ensuresglobal convergence

Summary of MATLAB MultipleVariable Methods Fminsearch: Find minimum of unconstrained multivariable functionusing derivative-free method Fminunc: Nonlinear programming solver. Finds minimum ofunconstrained multivariable function. Gradient and Hessian maybe supplied. Lsqnonlin: Solves nonlinear least-squares curve fitting problemsof the form

MATLAB DEMO: Banana Function MinimizationMinimize Rosenbrock's "banana function" f(x) is called the banana function becauseof its curvature around the origin.It is notorious in optimization examplesbecause of the slow convergence mostmethods exhibit when trying to solve thisproblemf(x) has a unique minimum at the point x [1, 1] where f(x) 0

Banana Function Minimization (cont.)This demo will show a number of ways to minimize f(x) starting atmultiple initial points.Demo Folder: BananaFunction Classical Optimization (Download filefrom Canvas)

16 A factory manufactures HONDA CITY and HONDA CIVIC cars. Determine the optimal number of HONDA CITY and HONDA CIVIC cars produced if the factory capacity is 90 cars per day, and the cost of manufacturing is C (x, y) 6x 2 12y, where x is the number of HONDA CITY cars and y is the number of HONDA CIVIC cars produced.

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