The Out-of-Kilter Algorithm and Its Applications to Network Flow Problems

The out-of-kilter algorithm, which was published by D.R. Fulkerson [1], is an algorithm that computes the solution to the minimum-cost flow problem in a flow network. To begin, the algorithm starts with an initial flow along the arcs and a number for each of the nodes in the network. By making use of Complementary Slackness Optimality Conditions (CSOC) [2], the algorithm searches for out-of-kilter arcs (those that do not satisfy CSOC conditions). If none are found the algorithm is complete. For arcs that do not satisfy the CSOC theorem, the flow needs to be increased or decreased to bring them into kilter. The algorithm will look for a path that either increases or decreases the flow according to the need. This is done until all arcs are in-kilter, at which point the algorithm is complete. If no paths are found to improve the system then there is no feasible flow. The Out-of-Kilter algorithm is applied to find the optimal solution to any problem that involves network flows. This includes problems such as transportation, assignment and shortest path problems. Computer solutions using a Pascal program and Matlab are demonstrated.

Sudrajat Setiawan

turbo pascal program language and material meaning of data structures for programmers

MENGENAL STRUKTUR DATA DAN PROGRAM PASCAL

•Struktur Data berfungsi untuk mengorganisasikan data sehingga penerapan atau pemeliharaan logika program menjadi lebih terstruktur.•Bahasa Program Turbo Pascal; Program adalah kumpulan intruksi yang disusun sehingga mempunyai urutan logika yang tepat. Salah satu aplikasi program tersebut adalah Turbo Pascal.•Algoritma dan Struktur DataAlgoritma merupakan urutan langkah sistematis untuk menyelesaikan masalah dengan usaha yang paling minimal.•Karakteristik Algoritma; Input, Output, Definite (jelas), Efective dan Terminate (Berakhir)•Langkah-langkah Pembuatan Program1.Mendefinisikan Permasalahan2.Membuat Rumusan untuk Pemecahan Masalah3.Implementasi & Coding4.Testing (Menguji Coba) dan Membuat Dokumentasi•Jenis-jenis Type Data1.Type Sederhanaa.Type ordinal (untuk semua bilangan kecuali bilngan real)b.Type real (untuk bilangan desimal)2.Type String (berisi sederetan karakter)3.Type Terstruktur (untuk ukuran tempat)a.Larik (Array)Contoh; VAR nilai : ARRAY [1..maks_msh] of charb.Rekaman (Record)Contoh; TYPE rec_msh = record Nim: string (10) ; Nama: string (20) ; Jur: string (15) End;c.Himpunand.Berkas4.Type PointerKata Kunci ; Algoritma dan Turbo Pascal

MENGENAL STRUKTUR DATA DAN PROGRAM PASCAL

•Struktur Data berfungsi untuk mengorganisasikan data sehingga penerapan atau pemeliharaan logika program menjadi lebih terstruktur.•Bahasa Program Turbo Pascal; Program adalah kumpulan intruksi yang disusun sehingga mempunyai urutan logika yang tepat. Salah satu aplikasi program tersebut adalah Turbo Pascal.•Algoritma dan Struktur DataAlgoritma merupakan urutan langkah sistematis untuk menyelesaikan masalah dengan usaha yang paling minimal.•Karakteristik Algoritma; Input, Output, Definite (jelas), Efective dan Terminate (Berakhir)•Langkah-langkah Pembuatan Program1.Mendefinisikan Permasalahan2.Membuat Rumusan untuk Pemecahan Masalah3.Implementasi & Coding4.Testing (Menguji Coba) dan Membuat Dokumentasi•Jenis-jenis Type Data1.Type Sederhanaa.Type ordinal (untuk semua bilangan kecuali bilngan real)b.Type real (untuk bilangan desimal)2.Type String (berisi sederetan karakter)3.Type Terstruktur (untuk ukuran tempat)a.Larik (Array)Contoh; VAR nilai : ARRAY [1..maks_msh] of charb.Rekaman (Record)Contoh; TYPE rec_msh = record Nim: string (10) ; Nama: string (20) ; Jur: string (15) End;c.Himpunand.Berkas4.Type Pointer

Comparing EEG/MEG neuroimaging methods based on localization error, false positive activity, and false positive connectivity

1.AbstractEEG/MEG neuroimaging consists of estimating the cortical distribution of time varying signals of electric neuronal activity, for the study of functional localization and connectivity. Currently, many different imaging methods are being used, with very different capabilities of correct localization of activity and of correct localization of connectivity. The aim here is to provide a guideline for choosing the best (i.e. least bad) imaging method. This first study is limited to the comparison of the following methods for EEG signals: sLORETA and eLORETA (standardized and exact low resolution electromagnetic tomography), MNE (minimum norm estimate), dSPM (dynamic statistical parametric mapping), and LCMVBs (linearly constrained minimum variance beamformers). These methods are linear, except for the LCMVBs that make use of the quadratic EEG covariances. To achieve a fair comparison, it is assumed here that the generators are independent and widely distributed (i.e. not few in number), giving a well-defined theoretical population EEG covariance matrix for use with the LCMVBs. Measures of localization error, false positive activity, and false positive connectivity are defined and computed under ideal no-noise conditions. It is empirically shown with extensive simulations that: (1) MNE, dSPM, and all LCMVBs are in general incapable of correct localization, while sLORETA and eLORETA have exact (zero-error) localization; (2) the brain volume with false positive activity is significantly larger for MN, dSPM, and all LCMVBs, as compared to sLORETA and eLORETA; and (3) the number of false positive connections is significantly larger for MN, dSPM, all LCMVBs, and sLORETA, as compared to eLORETA. Non-vague and fully detailed equations are given. PASCAL program codes and data files are available. It is noted that the results reported here do not apply to the LCMVBs based on EEG covariance matrices generated from extremely few generators, such as only one or two independent point sources.

An algorithm for a new method of change-point analysis in the independent Poisson sequence

Summary Step change-point and slope change-point models in the independent Poisson sequence are developed based on accumulated and doubly-accumulated statistics. The method for the step change-point model developed in Section 2 is an alternative to the likelihood ratio test of Worsley (1986) and the algorithm for p-value calculation based on the first-order Markov property is the same as that given there. Different algorithms for the non-null distribution and inference on the change-point itself are, however, newly developed and a Pascal program is given in the Appendix. These methods are extended to the slope change-point model in Section 3. The approach is essentially the same as that of Section 2 but the algorithm is now based on the second-order Markov property and becomes a little more complicated. The Pascal program related to the slope change-point model is supported on the website, URL: https://corec.meisei-u.ac.jp/labs/hirotsu/.

PENCOCOKAN KATA SECARA ACAK DENGAN METODE ALGORITMA GENETIKA MENGGUNAKAN PROGRAM PASCAL

The Genetic Algorithm was introduced independently for the rst time byHolland and De Jong in 1975. This algorithm is the stochastic optimization techniquesbased on the principle from evolution theory. Since then, numerous applications of ge-netic algorithm have been developed in many areas, such as scheduling and sequencing,reliability design, road network, and many others. This paper is addressed to discusssome steps performed in genetic algorithm processes. We also give an example of thealgorithm in word matching randomly using Pascal programming, which specically de-veloped for academic purposes. Some factors that inuence the accuracy of the resultingword matching at random is also explained in detail by running Pascal program repeat-edly.

Resolution methods in proving the program correctness

Program testing determines whether its behavior matches the specification, and also how it behaves in different exploitation conditions. Proving of program correctness is reduced to finding a proof for assertion that given sequence of formulas represents derivation within a formal theory of special predicted calculus. A well-known variant of this conception is described: correctness based on programming logic rules. It is shown that programming logic rules may be used in automatic resolution procedure. Illustrative examples are given, realized in prolog-like LP-language (with no restrictions to Horn's clauses and without the final failure). Basic information on LP-language are also given. It has been shown how a Pascal-program is being executed in LP-system proffer.