Linear Programming for Large-Scale Markov Decision Problems

We consider the problem of controlling a Markov decision process (MDP) with a large state space, so as to minimize average cost. Since it is intractable to compete with the optimal policy for large scale problems, we pursue the more modest goal of competing with a low-dimensional family of policies. We use the dual linear programming formulation of the MDP average cost problem, in which the variable is a stationary distribution over state-action pairs, and we consider a neighborhood of a low-dimensional subset of the set of stationary distributions (defined in terms of state-action features) as the comparison class. We propose two techniques, one based on stochastic convex optimization, and one based on constraint sampling. In both cases, we give bounds that show that the performance of our algorithms approaches the best achievable by any policy in the comparison class. Most importantly, these results depend on the size of the comparison class, but not on the size of the state space. Preliminary experiments show the effectiveness of the proposed algorithms in a queuing application.Comment: 27 pages, 3 figure

ANALISIS DATA KEMISKINAN DI JAWA TENGAH MENGGUNAKAN METODE MIXED GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSIONS (MGTWR)

Metode regresi merupakan salah satu metode statistika yang dapat digunakan untuk menganalisis data kemiskinan. Akan tetapi untuk data spasial model regresi biasa menjadi tidak sesuai. Salah satu metode regresi spasial yang digunakan untuk data spasial adalah Geographically Weighted Regression (GWR). Akan tetapi jika variabel waktu juga dimasukkan ke dalam model, maka model yang digunakan adalah Geographically and Temporally Weighted Regression (GTWR). Pada kenyataannya tidak semua variabel prediktor dalam model GWR mempunyai pengaruh secara spasial. Beberapa variabel prediktor berpengaruh secara global, sedangkan yang lainnya dapat mempertahankan pengaruh spasialnya. Oleh karena itu, model GWR dikembangkan menjadi model Mixed Geographically Weighted Regression (MGWR). Model MGWR merupakan gabungan dari model regresi linier global dengan model GWR. Hal ini berlaku juga untuk model GTWR yang dikembangkan menjadi model Mixed Geographically and Temporally Weighted Regression (MGTWR). Hasil penelitian menunnjukkan bahwa faktor-faktor yang mempengaruhi Persentase Kemiskinan di Jawa Tengah tahun 2010-2012 secara lokal adalah persentase keluarga prasejahtera. Sedangkan variabel Tingkat Partisipasi Angkatan Kerja, Indeks Pembangunan Manusia, Upah Minimum Regional dan Banyaknya Akte Pemilik Tanah hanya berpengaruh secara global pada semua lokasi pengamatan. Model MGTWR dengan pembobot fungsi kernel gaussian lebih layak digunakan untuk menganalisis tingkat kemiskinan di Jawa Tengah karena mempunyai nilai R2 terbesar. Kata Kunci : GWR, GTWR, MGWR, MGTWR, Regresi, Statistika Spasial, Kemiskinan

Prediction of Weekly Rainfall in Semarang City Use Support Vector Regression (SVR) with Quadratic Loss Function

Semarang city is one of the busiest city in Indonesia. Doe to its role as the capital city of Central Java, Semarang is known as having a relativity high rate economic activities. The geographic of Semarang city bordered by the Java sea, thus whenever the rainfall is high, there could be flood at certain area. Therefore, prediction of rainfall is very important. Support vector machine (SVM) is one of the most popular methods in nonlinear approach. One of the branches of this method for prediction is support vector regression (SVR). SVR can be approached by quadratic loss function. The study is focus on Semarang rainfall prediction during 2009 to 2013 using several kernel function. Kernel Function can provide optimal weight Some of kernel functions are linear, polynomial, and Radial Basis Function (RBF). Using this method, the study provide 71.61% R-square in the training data, for C parameter 2 with polynomial (p=2), and 71.46% R-square for the testing dat

Geographically Weighted Regression Principal Component Analysis (Gwrpca) Pada Pemodelan Pendapatan Asli Daerah Di Jawa Tengah

Linear Regression Analysis is a method for modeling the relation between a response variable with two or more independent variables. Geographically Weighted Regression (GWR) is a development of the regression model where each observation location has different regression parameter values because of the effects of spatial heterogenity. Regression Principal Component Analysis (PCA) is a combination of PCA and are used to remove the effect of multicolinearity in regression. Geographically Weighted Regression Principal Component Analysis (GWRPCA) is a combination of PCA and GWR if spatial heterogenity and local multicolinearity occured. Estimation parameters for the GWR and GWRPCA using Weighted Least Square (WLS). Weighting use fixed gaussian kernel function through selection of the optimum bandwidth is 0,08321242 with minimum Cross Validation (CV) is 3,009035. There are some variables in PCA that affect locally-generated revenue in Central Java on 2012 and 2013, which can be represented by PC1 that explained the total variance data about 71,4%. GWRPCA is a better model for modeling locally-generated revenue for the districts and cities in Central Java than RPCA because it has the the smallest Akaike Information Criterion (AIC) and the largest R2

Analisis Support Vector Regression (Svr) dalam Memprediksi Kurs Rupiah terhadap Dollar Amerika Serikat

In economy, the global markets have an important role as a forum for International transactions between countries in selling or purchasing goods or services on an International scale. Money as legal tender in the trading activities, but the problem is the difference between the state of the currency, the exchange rate will be established. Exchange rate is the value of a country\u27s currency is expressed in another country\u27s currency value. Fluctuations in foreign exchange rates greatly affect the Indonesian economy, so the determination of the exchange rate should be beneficial to a country can run the economy well. To predict the exchange rate of the Rupiah against the United States dollar in this study used methods of Support Vector Regression (SVR) is a technique to predict the output in the form of continuous data. SVR aims to find a hyperplane (line separator) in the form of the best regression function is used to predict the exchange rate against the United States dollar with linear kernel and polynomial functions. Criteria used in measuring the goodness of the model is the MAPE (Mean Absolute Percentage Error) and R2 (coefficient of determination). The results of this study indicate that both the kernel function gives very good accuracy in the prediction results of the exchange rate with R2 of 99.99% with MAPE 0.6131% in the kernel linear and R2 result of 99.99% with MAPE 0.6135% in the kernel polynomial

Prediksi Harga Saham Menggunakan Support Vector Regression dengan Algoritma Grid Search

The stock market has become a popular investment channel in recent years because of the low return rates of other investment. The stock price prediction is in the interest of both private and institution investors. Accurate forecasting of stock prices is an appealing yet difficult activity in the business world. Therefore, stock prices forecasting is regarded as one of the most challenging topics in business. The forecasting techniques used in the literature can be classified into two categories: linear models and non linear models. One of forecasting techniques in nonlinear models is support vector regression (SVR). Basically, SVR adopts the structural risk minimization principle to estimate a function by minimizing an upper bound of the generalization. The optimal parameters of SVR can be use Grid Search Algorithm method. Concept of this method is using cross validation (CV). In this paper, the SVR model use linear kernel function. The accurate prediction of stock price, in telecommunication, is 92.47% for training data and 83.39% for testing data

Pemodelan General Regression Neural Network (Grnn) Pada Data Return Indeks Harga Saham Euro 50

General Regression Neural Network (GRNN) merupakan salah satu model jaringan radial basis yang digunakan untuk pendekatan suatu fungsi. Model GRNN termasuk model jaringan syaraf tiruan dengan solusi yang cepat, karena tidak diperlukan iterasi yang besar pada estimasi bobot-bobotnya. Model ini memiliki arsitektur jaringan yang baku, dimana jumlah unit pada pattern layer sesuai dengan jumlah data input. Salah satu aplikasi GRNN adalah untuk memprediksi nilai return saham dari indeks Euro 50 CFD (Contract For Difference). Indeks Euro 50 CFD (Contract For Difference) digunakan sebagai patokan harga saham dari 50 Perusahaan terbesar di zona Eropa. Para investor melakukan investasi di saham indeks Euro 50 CFD (Contract For Difference) dengan harapan mendapatkan kembali keuntungan yang sesuai dengan apa yang telah di investasikannya. Dengan menggunakan model GRNN diperoleh bahwa nilai RMSE dan R2 untuk data training sebesar 0,00095 dan 99,19%. Untuk data testing diperoleh nilai RMSE dan R2 sebesar 0,00725 dan 98,46%. Berdasarkan nilai prediksi return saham dua belas hari ke depan diperoleh kerugian tertinggi atau capital loss pada 15 Desember 2014 sebesar 5,583188% dan profit tertinggi atau capital gain pada tanggal 10 Desember 2014 sebesar 2,267641% Kata Kunci: GRNN, Jaringan Syaraf Tiruan, Return Saham, Indeks Euro 50, Kerugian Tertinggi, Profit Tertinggi, Prediks

PERBANDINGAN MODEL JARINGAN SYARAF TIRUAN DENGAN ALGORITMA LEVENBERG-MARQUADT DAN POWELL-BEALE CONJUGATE GRADIENTPADA KECEPATAN ANGIN RATA-RATA DI KOTA SEMARANG

Wind is one of the most important weather components. Wind is defined as the dynamics of horizontal air mass displacement measured in two parameters, namely speed and direction. Wind speed and direction depend on the air pressure conditions around the place. High wind speed intensity can cause high sea water waves. To estimate wind speed intensity required a study of wind speed prediction. One of method that can be used is Artificial Neural Network (ANN). In ANN there are several models, one of which is backpropagation. Thepurpose of this researchis to compare between backpropagation model with Levenberg-Marquadt and Powell-Beale Conjugate Gradient algorithms. The results of this researchshowing that Powell-Beale Conjugate Gradient better than Levenberg-Marquadtalgorithms. The best model architecture obtained is a network with two input layer neurons, six hidden layer neurons, and one output layer neuron. The activation function used are the logistic sigmoid in the hidden layer and linear in the output layer. MAPE value based on the chosen model is 0,0136% in training process and 0,0088% in testing process

The combined S velocity achieved from tricuspid annulus and pulmonary annulus with tissue Doppler imaging could predict the proximal right coronary artery occlusion in patients with inferior myocardial infarction

Aim: To investigate if combined S velocity (CSV) calculated from tricuspid annulus and pulmonary annulus with tissue Doppler imaging in individuals with acute inferior myocardial infarction were linked to proximal RCA lesions. Methods: The study comprised 48 patient who had been diagnosed with acute inferior myocardial infarction and had culprit lesions in the right coronary artery. The RCA occlusion in Group A was proximal to the right ventricular branch, while the RCA occlusion in Group B was distant to the RV branch. The combined S velocity was tested, as well as other echocardiographic parameters. Results: In terms of metrics indicating right ventricular function, there were substantial disparities between the groups. A favorable association was established in the univariate correlation analysis between CSV and tissue Doppler imaging derived tricuspid annulus systolic velocity (St), pulmonary annulus motion velocity evaluated by TDI (PAMVUT), RV tricuspid annular plane systolic excursion (TAPSE), and fractional area change (FAC). CSV was identified as an independent predictor of proximal RCA occlusion in a multivariate logistic regression test. In the ROC analysis, CSV<18.3 cm/s and PAMVUT<8.6 cm/s indicated proximal RCA occlusion with 83 percent sensitivity and 71 percent specificity (AUC=0.83, p<0.001), and 85 percent sensitivity and 71 percent specificity (AUC=0.81, p<0.001), respectively. Conclusion: CSV measurements were revealed to be an important predictor of proximal RCA occlusions in this investigation