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# Stochastic Approximation and Simulated Annealing

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AACIMP 2010 Summer School lecture by Leonidas Sakalauskas. "Applied Mathematics" stream. "Stochastic Programming and Applications" course. Part 8.

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### Stochastic Approximation and Simulated Annealing

1. 1. Lecture 8 Stochastic Approximation and Simulated Annealing Leonidas Sakalauskas Institute of Mathematics and Informatics Vilnius, Lithuania <sakal@ktl.mii.lt> EURO Working Group on Continuous Optimization
2. 2. Content Introduction. Stochastic Approximation: SPSA with Lipschitz perturbation operator; SPSA with Uniform perturbation operator; Standard Finite Difference Approximation algorithm. Simulated Annealing Implementation and Applications Wrap-Up and Conclusions
3. 3. Introduction In many practical problems of technical design some of the data may be subject to significant uncertainty which is reduced to probabilistic- statistical models. The performance of such problems can be viewed like constrained stochastic optimization programming tasks. Stochastic Approximation can be considered as alternative to traditional optimization methods, especially when objective functions are no differentiable or computed with noise.
4. 4. Stochastic Approximation Application of Stochastic Approximation to solving of optimization problems, while the objective function is non-differentiable or nonsmooth and computed with noise is a topical theoretical and practical problem. The known methods of Stochastic Approximation for solving of these problems use the idea of stochastic gradient and certain rules of changing of step length for ensuring the convergence.
5. 5. Formulation of the optimization problem The optimization problem is (minimization) as follows: f x min x n where f : is a bounded from below Lipshitz n function.
6. 6. Formulation of the optimization problem Let f ( x ) be generalized gradient of this function. Assume X * to be a set of stationary points: and F * to be a set of function values: X* x0 f x , F* zz f x ,x X* .
7. 7. We consider a function smoothed by perturbation operator: f x, Ef x , ~p where 0 is the value of the perturbation parameter. The functions smoothed by this operator are twice continuously differentiable (Rubinstein & Shapiro (1993), Bartkute & Sakalauskas (2004)), that offers certain opportunities creating optimization algorithms.
8. 8. Advantages of SPSA At last time the interesting research was focussed on Simulated Perturbation Stochastic Approximation (SPSA) It is enough to calculate values of the function only in one or some points for the estimation of the stochastic gradient in SPSA algorithms, that promises for us to reduce numerical complexity of optimization.
9. 9. SA algorithms 1. SPSA with Lipschitz perturbation operator. 2. SPSA with Uniform perturbation operator. 3. Standard Finite Difference Approximation algorithm.
10. 10. General Stochastic Approximation scheme xk 1 xk k g k , k 1, 2, ... where g k g xk , k , k stochastic gradient and g x, E g x, , , g x, g x , 0. This scheme is the same for different Stochastic Approximation algorithms whose distinguish only by approach for stochastic gradient estimation.
11. 11. SPSA with Lipschitz perturbation operator Gradient estimator of the SPSA with Lipschitz perturbation operator is expressed as: f x f x g x, , where -is the value of the perturbation parameter, vector -is uniformly distributed in the unit ball 1 , if y 1, y Vn 0 , if y 1. Vn -is the volume of the n-dimensional ball (Bartkute & Sakalauskas (2007))
12. 12. SPSA with Uniform perturbation operator Gradient estimator of the SPSA with Uniform perturbation operator is expressed as: f x f x g x, , 2 where -is the value of the perturbation parameter, 1, 2 , .... , n -is a vector consisting of variables uniformly distributed from the interval [-1;1] (Mikhalevitch et al (1987)).
13. 13. Standard Finite Difference Approximation algorithm Gradient estimator of the Standard Finite Difference Approximation algorithm is expressed as: f x i f x gi x , , , where -is the value of the perturbation parameter, vector -is uniformly distributed in the unit ball; i 0,0,0,....,1,.....,0 -is the vector with zero components except ith one, which is equal to 1. (Mikhalevitch et al (1987)).
14. 14. Rate of convergence Let consider that the function f(x) has a sharp minimum in the point x * , in which the algorithm converges a b , a 0, k , b 0, 0 a 1 k 1, . when k k b 2 H k 1 * 2 A K2 a b 1 1 E x x o 2 aH , k 1 H k1 b k Then where A>0, H>0, K>0 are certain constants, x* k 1 is minimum point of the smoothed function.
15. 15. Computer simulation The proposed methods were tested with following functions: n f ak xk M k 1 where ak is a set of real numbers randomly and uniformly generated in the interval ,K , K 0. The samples of T=500 test functions were generated, when 2, K 5.
16. 16. Empirical and theoretical rates of convergence by SA methods 0.5 0.75 0.9 Theoretical 1.5 1.75 1.9 rates Empirical rates SPSA (Lipshitz perturbation) n=2 1.45509 1.72013 1.892668 n=4 1.41801 1.74426 1.958998 SPSA ( Uniform perturbation) n=2 1.605244 1.938319 1.988265 n=4 1.551486 1.784519 1.998132 Stochastic Difference Approximation method n=2 1.52799 1.76399 1.90479 n=4 1.50236 1.75057 1.90621
17. 17. The rate of convergence (n = 2) 2 E xk x*
18. 18. The rate of convergence (n = 10) 2 E xk x*
19. 19. Volatility estimation by Stochastic Approximation algorithm Let us consider the application of SA to the minimization of the mean absolute pricing error for the parameter calibration in the Heston Stochastic Volatility model [Heston S. L.(1993)]. We consider the mean absolute pricing error (MAE) defined as : N 1 MAE , , , , v, CiH , , , , v, Ci N i 1 where N is the total number of options, C i and CiH represent the realized market price and the implied the theoretical model price, respectively, while , , , , v, (n=6) are the parameters of the Heston model to be estimated.
20. 20. To compute option prices by the Heston model, one needs input parameters that can hardly be found from the market data. We need to estimate the above parameters by an appropriate calibration procedure. The estimates of the Heston model parameters are obtained by minimizing MAE: MAE , , , , v, min Let consider the Heston model for the Call option on SPX (29 May 2002).
21. 21. Minimization of the mean absolute pricing error by SPSA and SFDA methods
22. 22. Optimal Design of Cargo Oil Tankers In cargo oil tankers design, it is necessary to choose such sizes for bulkheads, that the weight of bulkheads would be minimal.
23. 23. The minimization of weight of bulkheads for the cargo oil tank we can formulate like nonlinear programing task (Reklaitis et al (1986)): 5.885 x4 x1 x3 f x min 2 2 x1 x 3 x 2 subject to 1 2 2 g1 x x2 x4 0.4 x1 x3 8.94 x1 x3 x2 0 6 4 2 1 2 2 3 g2 x x x 4 0.2 x1 2 x3 2.2 8.94 x1 x 3 x 2 0 12 g3 x x4 0.0156 x1 0.15 0 g4 x x4 0.0156 x3 0.15 0 g5 x x4 1.05 0 g6 x x3 x2 0 where x1- width, x2 -debt, x 3 - lenght, x4 - thikness.
24. 24. SPSA with Lipschitz perturbation for the cargo oil target design 7.5 7.4 7.3 7.2 7.1 7 6.9 6.8 6.7 6.6 6.5 100 1000 1900 2800 3700 4600 5500 6400 7300 8200 9100 10000 Number of iterations
25. 25. Confidence bounds of the minimum (A=6.84241, T=100, N=1000) 7.1 Upper bound 7 6.9 Lower bound 6.8 Minimum of the 6.7 objective function 6.6 6.5 6.4 2 102 202 302 402 502 602 702 802 902 Number of iterations
26. 26. Simulated Annealing Global optimization methods  Global algorithms (bounds and branch algorithms, dynamic programming, full selection, etc)  Greedy optimization (local search)  Heuristic optimization
27. 27. Metaheuristics  Simulated Annealing  Genetic Algorithms  Swarm Intelligence  Ant Colony  Taboo search  Scatter search  Variable neighborhood  Neural Networks  Etc.
28. 28. Simulated Annealing algorithm Simulated Annealing algorithm is developed by modeling steel annealing process (Metropolis et al. (1953)) A lot of applications in Operational Research and Data Analysis, etc.
29. 29. Simulated Annealing Main idea: to simulate drift of current solution with probability distribution P( x, T k ) to improve solution updating - temperature function Tk - neighborhood function k
30. 30. Simulated Annealing algorithm 0 Step 1. Choose , T x , set k 0, 0. 0 Step 2. Generate drift Z k 1 with probability distribution P( x, T k ) Step 3. If Zk 1 k and f ( xk ) f ( xk Z k 1 ) (Metropolis rule) Tk e  U (0,1) then accept: k 1 k k 1 ; k=k+1; otherwise Step 2 x x Z
31. 31. Improvement of SA by Pareto Type models The theoretical investigation of SA convergence shows, that in these algorithms Pareto type models can be applied to form search sequence (Yang (2000)). Class of Pareto models, main feature and parameter: Pareto model’s distributions have "heavy tails“. α - the main parameter of these models, which impacts the heaviness of the tail α –stable distributions are Pareto (follows to C.L.T.)
32. 32. Pareto type (Heavy-tailed) distributions Main features: infinite variance, infinite mean Introduced by Pareto in the 1920’s Mandelbrot established the use of heavy-tailed distributions to model real-world fractal phenomena. There are a lot of other applications (financial market, traffic in computer and telecommunication networks, etc.).
33. 33. Pareto type (Heavy-tailed) distributions Heavy-Tailed - Power Law has polynomial decay (e.g. Pareto-Levy): P{X x}~ Cx ,x 0 where 0 < α < 2 and C > 0 are constants
34. 34. - stable distributions
35. 35. Comparison of tail probabilities for standard normal, Cauchy and Levy distributions In this table were compared the tail probabilities for the three distributions. It is clear that the tail probability for the normal quickly becomes negligible, whereas the other two distributions have a significant probability mass in the tail.
36. 36. Improvement of SA by Pareto type models The convergence conditions (Yang (2000)) indicate that, under suitable conditions, an appropriate choice of the temperature and neighborhood size updating functions ensures the convergence of the SA algorithm to the global minimum of the objective function over the domain of interest. The following corollaries give different forms of temperature and neighborhood size updating functions corresponding to different kinds of generation probability density functions to guarantee the global convergence of the SA algorithm.
37. 37. Convergence of Simulated Annealing
38. 38. Improvement of SA in continuous optimization The above corollaries indicate that a different form of temperature updating function has to be used with respect to a different kind of generation probability density function in order to ensure the global convergence of the corresponding SA algorithm.
39. 39. Convergence of Simulated Annealing Some Pareto-type models , Table 1.
40. 40. Convergence of Simulated Annealing
41. 41. Testing of SA for continuous optimization In global and combinatorial optimization problems, when optimization algorithms are used, the reliability and efficiency of these algorithms is needed to be tested. Special testing functions, known in literature, are used for this. Some of these functions have one or more global minimum, some of them have global and local minimums. With the help of these functions it can be ensured, that the methods are efficient enough, thus, it is possible to test and prevent algorithms from being trapped in local minimum, as well as the speed and accuracy of convergence and other parameters can be watched.
42. 42. Testing criteria By modeling SA algorithm with some testing functions with two different distributions, and changing some optional parameters, there were some questions:  which of these distributions guarantees the faster convergence to global minimum by value of objective function;  what are probabilities of finding global minimum, how can impact these probabilities the changing of some parameters;  what the proper number of iterations, which guarantees the finding global minimum with desirable probability.
43. 43. Testing criteria Characteristics to be evaluated by Monte-Carlo simulation:  value of minimized objective function;  probability to find global minimum after some number of iterations. These characteristics were computed by Monte-Carlo method - N realizations (N=100, 500, 1000) with K iterations each (K=100, 500, 1000, 3000, 10000, 30000).
44. 44. Test functions An example of test function: Branin’s RCOS (RC) function (2 variables): RC(x1,x2)=(x2-(5/(4 2))x12+(5/ )x1- 6)2+10(1-(1/(8 )))cos(x1)+10; Search domain: 5 < x1 < 10, 0 < x2 < 15; 3 minima: (x1 , x2)*=(- , 12.275), ( , 2.275), (9.42478 , 2.475); RC((x1 , x2)*)=0.397887.
45. 45. Simulation results
46. 46. Simulation results
47. 47. Simulation results
48. 48. Simulation results 1 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0 1 200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000 Iteracijų skaičius Fig. 1. Probability to find global minimum by SA for Rastrigin function
49. 49. Wrap-Up and Conclusions 1. The SA methods have been considered for comparison SPSA with Lipschitz perturbation operator; SPSA with Uniform perturbation operator and SFDA method as well Simulated Annealing; 2. Computer simulation by Monte-Carlo method has shown that the empirical estimates of the rate of convergence of SA for nondifferentiable functions corroborate the theoretical rates O 1 , 1 2 k