Multiple Gradient Descent Algorithm

Outline Introduction Multiple Gradient Descent Algorithm MGDA for non-smooth convex problems

Multiple Gradient Descent Algorithm

Outi Wilppu

March 1, 2013

Outi Wilppu Multiple Gradient Descent Algorithm


1 Introduction

2 Multiple Gradient Descent AlgorithmSteepest Descent MethodTheoretical backroundAlgorithm

3 MGDA for non-smooth convex problemsTheoretical backroundAlgorithmExampleFuture work



Motivation

Many real life problems have multiobjective nature

• Problem has several goals such that they should all be asgood as possible

• Goals are conflicting and have to make compromises

• For example in economics and engineering

• Many problems have also non-smooth nature



Problem

Definition

We consider a multiobjective problem of form

min f1(x), . . . , fk(x) (1)

s. t. x ∈ S

where

• Objective functions fi (x) : Rn −→ R• Set S ⊆ Rn is a set of feasible solutions



Pareto optimality

Definition

A solution x∗ ∈ S is Pareto optimal if there is no other solutionx ∈ S such that fi (x) ≤ fi (x

∗) for all i = 1, . . . , k andfi (x) < fi (x

∗) for at least one index i .

• Usually there exists a lot of Pareto optimal solutions, calledPareto optimal set

• All Pareto optimal solution are mathematically equally good



Steepest Descent Method

• A method to solve smooth unconstrained optimizationproblem with single objective function

min f (x), s. t. x ∈ Rn

• Search direction d = − ∇f (x)

‖ ∇f (x) ‖, which is the steepest

descent direction at the point x

• Stepsize λ can be found with line search

• A new point is of the form x+ = x + λd, wheref (x+) < f (x) until the optimum is reached



About MGDA

• Assume that in problem (1) we have

– All objective functions fi are continiously differentiable– Problem has no constrains and feasible set is Rn

• The main idea is to find a common descent direction for allobjective functions by defining the convex hull of gradients ofobjective functions and finding the minimum norm element ofthe convex hull

• With this method we obtain one Pareto stationary solution



Pareto stationary

Definition

Objective functions fi are said to be Pareto stationary at the pointx∗ if

k∑i=1

αi∇fi (x∗) = 0, αi ≥ 0 ∀i ,

k∑i=1

αi = 1.

• Pareto stationarity is a necessary condition for Paretooptimality



Theorem

Let objective funtions fi , i = 1, . . . , k be continiously differentiableand

U = {w ∈ Rn | w =k∑

i=1

αi∇fi (x), αi ≥ 0 ∀ i ,k∑

i=1

αi = 1}

Let vector w∗ be a minimum norm element of convex hull of U i.e.w∗ = argmin{‖ w ‖ |w ∈ conv U}. Then

1 either w∗ = 0 and objective functions fi , i = 1, . . . , k arePareto stationary at the point x∗

2 or w∗ 6= 0 and −w∗ is common descent direction for allobjective functions. Additionally, if w∗ ∈ U thenuTw∗ =‖ w∗ ‖2 for all u ∈ conv U.



Search direction

• To find the vector w we need to solve the problem

min ‖k∑

i=1

αi∇fi (x)‖2

s. t. αi ≥ 0∀ i ,k∑

i=1

αi = 1.

• In case of two objective functions we have

α1 =

‖v‖2−uTv

‖u‖2+‖v‖2−2uTv, if uTv < min{‖u‖2, ‖v‖2}

0, if min{‖u‖, ‖v‖} = ‖v‖1, if min{‖u‖, ‖v‖} = ‖u‖

where u = ∇f1(x), v = ∇f2(x) and α2 = 1− α1.



Stepsize

• Stepsize λ is the largest strictly positive real number such thatall the functions gi (t) = fi (x− tw) are monotonicallydecreasing over the interval [0, t].

• A new point x+ is x+ = x− λw• Algorithm stops when w = 0



About MGDA for non-smooth convex problems

• Assume that in the problem (1)

– Objective functions fi are convex functions, not necessarilydifferentiable

– Problem has no constrains and the feasible set is Rn

– The subdifferentials of objective functions fi are known

• The basic idea is the same as before but now instead ofgradients we use the steepest descent subgradients



Subdifferential and subgradient

Definition

Let function f be convex. A subdifferential of f at the point x is aset

∂f (x) = {ξ ∈ Rn | f ′(x;d) ≥ ξTd}

for all d ∈ Rn. A vector ξ ∈ ∂f (x) is called subgradient offunction f at the point x.

Theorem

Let function f : Rn −→ R be convex and differentiable at the pointx ∈ Rn. Then

∂f (x) = {∇f (x)}.



Steepest descent direction

• To determine the steepest descent direction we need to solvethe problem

min f ′(x;d) s. t. ‖d‖ = 1 (2)

• The problem (2) can be written

min ‖ξ‖ s. t. ξ ∈ ∂f (x)

• Thus the steepest descent direction at the point x is thesmallest norm subgradient in the subdifferential ∂f (x)



Algorithm

• To obtain a search direction we need to define the steepestdescent subgradient for every objective function at the pointx. After that we can solve problem

min ‖k∑

i=1

αiξi‖2

s. t. αi ≥ 0 ∀ i ,k∑

i=1

αi = 1

where ξi ∈ ∂fi (x) is the steepest descent subgradient.

• Stepsize and new point are determined as in smooth case



• With this method we obtain one Pareto stationary solutionsince

k∑i=1

αiξi = 0, αi ≥ 0 and

k∑i=1

αi = 1.

• In convex case Pareto stationarity is necessary and sufficientcondition to Pareto optimality

⇒ We obtain one Pareto optimal solution



Example

Consider the following problem:

min {f1(x), f2(x)}s. t. x ∈ R2

where

f1(x) = max{x21 + (x2 − 1)2, (x1 + 1)2}

f2(x) = max{2x1 − x2, x21 + x2}

and starting point x0 = (1, 0.5).



The steepest descent subgradients at the point x0:

– ξ1 = (4, 0)

– To determine ξ2 we need to find ρ ∈ [0, 1] such that we findthe minimum norm of subdifferential

∂f2(x0) = ρ(2,−1) + (1− ρ)(2, 1) = (2, 1− 2ρ).

To obtain the steepest descent subgradient ρ =1

2and

ξ2 = (2, 0).



A common descent search direction:

– w = αξ1 + (1− α)ξ2

– We obtain α from formula

α =

‖ξ2‖2−ξT1 ξ2

‖ξ1‖2+‖ξ2‖2−2ξT1 ξ2, if ξT1 ξ2 < min{‖ξ1‖2, ‖ξ2‖2}

0, if min{‖ξ1‖, ‖ξ2‖} = ‖ξ2‖1, if min{‖ξ1‖, ‖ξ2‖} = ‖ξ1‖

– Thus w = (2, 0)



Stepsize:

– Function g1(t) = f1(x0 − tw) is descent if t ∈ [0, 0.6875]

– Function g2(t) = f2(x0 − tw) is descent if t ∈ [0, 0.5]

⇒ Stepsize λ = 0.5

New point:

– x1 = x0 − λw = (0, 0.5)



k ξ1 ξ2 α w λ xk+1

0 (4, 0) (2, 0) 0 (2, 0) 0.5 (0, 0.5)

1 (2, 0) (0, 1) 0.2 (0.4, 0.8) 0.403 (−0.161, 0.178)

2 (1.678, 0) (−0.322, 1) 0.329 (0.336; 0.671)

Solution: x∗ = (−0.161, 0.178), f1(x∗) ≈ 0.704 andf2(x∗) ≈ 0.203



Future work

• The biggest drawback is that we need to know wholesubdifferential

• The aim is to find an effective way to get a common descentdirection for all objective functions

• We do not need more than one arbitrary subgradient offunction



Thank you for your attention!


Multiple Gradient Descent Algorithm

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