scholarly journals Diversifikasi dan Optimasi Usahatani Terintegrasi pada Lahan Kering di Desa Musi Kecamatan Gerokgak Kabupaten Buleleng

2021 ◽  
Vol 9 (2) ◽  
pp. 416
Author(s):  
WISNO WARDANA ◽  
I Wayan Budiasa ◽  
I Ketut Suamba
Keyword(s):  
Linear Programming ◽  
Dual Problem ◽  
Problem Solution ◽  
Gross Margin ◽  
Primal Problem

Tujuan penelitian adalah (1) menganalisis besarnya pendapatan aktual (gross margin) usahatani terintegrasi (2) menganalisis apakah diversifikasi usahatani pada usahatani terintegrasi lahan kering sudah optimal. Metode yang digunakan dalam menentukan sampel pada penilitian ini adalah teknik sensus sample. Teknik sampel ini menggunakan semua anggota SIMANTRI 001 sebagai sampel dengan anggota kelompok sebanyak 20 orang. Analisis pendapatan aktual yang dipergunakan adalah analisis usahatani melalui perhitungan gross margin. Analisis optimasi dan pendapatan maksimun dianalisis menggunakan metode linear programming (LP) yang diselesaikan dengan bantuan software BPLX88. Hasil penelitian menunjukkan bahwa berdasarkan hasil analisis gross margin, dengan rata-rata luas lahan kering sebesar 0,497 ha, diperoleh pendapatan aktual usahatani jagung MT-1, jagung MT-2, kacang tanah dan ternak sapi sebesar Rp. 696.326.650 per tahun. Berdasarkan hasil analisis linear programming yang dilihat dari primal problem solution menunjukkan jagung (PJG1), jagung  (PJG2), kacang tanah (PKT) dan sapi (PSAPI) yang diusahakan bersatus basic atau profitable. Hal ini menunjukkan bahwa lahan seluas 0,497 ha telah berkontribusi dalam memperoleh pendapatan maksimum sebesar Rp. 697.333.800 per tahun. Selanjutnya pada dual problem solution, semua kendala lahan per cabang usahatani dengan luas lahan masing-masing tanaman sebesar 9,95 ha telah habis terpakai, Hal ini menunjukkan bahwa kendala lahan jagung MT-1, jagung MT-2, dan kacang tanah berstatus binding atau habis terpakai tanpa ada sisa (slack). Namun sebagian kendala tidak bersifat binding hal ini terlihat pada stok tenaga kerja bulan Januari-Desember yang belum habis digunakan. Berdasarkan analisis optimasi melalui metode linear programming dengan bantuan BLPXX8 terselenggara dengan optimal, hal ini terbukti dengan pendapatan maksimum sebesar Rp. 697.334.000 artinya mengalami peningakatan pendapatan sebesar Rp.1.007.350 (0,14%), dari pendapataan aktual saat penelitiaan sebesar Rp.696.326.650.

The use of linear programming to investigate the impact of changes in feed price and milk value on gross margin of Taiwanese dairy farms

2005 ◽  
Vol 2005 ◽  
pp. 116-116
Author(s):  
P. Chang ◽  
P. Rowlinson ◽  
P. Cain
Keyword(s):  
Linear Programming ◽  
Dairy Farm ◽  
World Trade Organisation ◽  
Open Market ◽  
Price Support ◽  
Gross Margin ◽  
Domestic Prices ◽  
Price Trends ◽  
Import Restrictions ◽  
The Impact

The Taiwanese government assists their dairy industry by supporting domestic prices through a combination of import restrictions, price support, government purchasing and subsidised disposal of surpluses. As a result, dairy production in Taiwan is insulated from international price trends. However, these dairy assistance policies can not protect the domestic price after Taiwan joins the World Trade Organisation (WTO) at the end of 2001. To access the impact of an open market, linear programming (LP) is applied to model the influence of changing feed price and milk value on dairy farm profitability.

A dual simplex method for grey linear programming problems based on duality results

2020 ◽  
Vol 10 (2) ◽  
pp. 145-157
Author(s):  
Davood Darvishi Salookolaei ◽  
Seyed Hadi Nasseri
Keyword(s):  
Linear Programming ◽  
Programming Problem ◽  
Linear Programming Problem ◽  
Simplex Method ◽  
Dual Problem ◽  
Complementary Slackness ◽  
Dual Simplex Method ◽  
Content Type ◽  
Dual Simplex ◽  
Linear Programming Problems

PurposeFor extending the common definitions and concepts of grey system theory to the optimization subject, a dual problem is proposed for the primal grey linear programming problem.Design/methodology/approachThe authors discuss the solution concepts of primal and dual of grey linear programming problems without converting them to classical linear programming problems. A numerical example is provided to illustrate the theory developed.FindingsBy using arithmetic operations between interval grey numbers, the authors prove the complementary slackness theorem for grey linear programming problem and the associated dual problem.Originality/valueComplementary slackness theorem for grey linear programming is first presented and proven. After that, a dual simplex method in grey environment is introduced and then some useful concepts are presented.

scholarly journals ROBUST EXPONENTIAL HEDGING AND INDIFFERENCE VALUATION

2010 ◽  
Vol 13 (07) ◽  
pp. 1075-1101 ◽  
Author(s):  
KEITA OWARI
Keyword(s):  
Dual Problem ◽  
Maximization Problem ◽  
Probability Measures ◽  
Primal Problem ◽  
Robust Version ◽  
Indifference Valuation ◽  
Robust Utility Maximization ◽  
Indifference Price ◽  
Utility Indifference ◽  
Utility Maximization Problem

We discuss the problem of exponential hedging in the presence of model uncertainty expressed by a set of probability measures. This is a robust utility maximization problem with a contingent claim. We first consider the dual problem which is the minimization of penalized relative entropy over a product set of probability measures, showing the existence and variational characterizations of the solution. These results are applied to the primal problem. Then we consider the robust version of exponential utility indifference valuation, giving the representation of indifference price using a duality result.

scholarly journals An Improved Subgradiend Optimization Technique for Solving IPs with Lagrangean Relaxation

2013 ◽  
Vol 61 (2) ◽  
pp. 135-140
Author(s):  
M Babul Hasan ◽  
Md Toha
Keyword(s):  
Dual Problem ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Concave Function ◽  
Optimization Method ◽  
Duality Gap ◽  
Dual Function ◽  
Subgradient Optimization ◽  
Step Size ◽  
Primal Problem

The objective of this paper is to improve the subgradient optimization method which is used to solve non-differentiable optimization problems in the Lagrangian dual problem. One of the main drawbacks of the subgradient method is the tuning process to determine the sequence of step-lengths to update successive iterates. In this paper, we propose a modified subgradient optimization method with various step size rules to compute a tuning- free subgradient step-length that is geometrically motivated and algebraically deduced. It is well known that the dual function is a concave function over its domain (regardless of the structure of the cost and constraints of the primal problem), but not necessarily differentiable. We solve the dual problem whenever it is easier to solve than the primal problem with no duality gap. However, even if there is a duality gap the solution of the dual problem provides a lower bound to the primal optimum that can be useful in combinatorial optimization. Numerical examples are illustrated to demonstrate the method. DOI: http://dx.doi.org/10.3329/dujs.v61i2.17059 Dhaka Univ. J. Sci. 61(2): 135-140, 2013 (July)

An Extended Simplex Algorithm for Linear Programming

2012 ◽  
Vol 532-533 ◽  
pp. 1626-1630
Author(s):  
Guo Guang Zhang
Keyword(s):  
Linear Programming ◽  
Simplex Method ◽  
Feasible Solution ◽  
Simplex Algorithm ◽  
Linear Program ◽  
Problem Solution ◽  
Test Number ◽  
The Times ◽  
Definition Of

Simplex method is one of the most useful methods to solve linear program. However, before using the simplex method, it is required to have a base feasible solution of linear program and the linear program is changed to thetypical form. Although there are some methods to gain the base feasible solution of linear program, artificial variablesare added and the times of calculating are increased with these calculations. In this paper, an extended algorithm of the simplex algorithm is established, the definition of feasible solution in the new algorithm is expended, the test number is not the same sign in the process of finding problem solution. Explained the principle of the new algorithm and showed results of LP problems calculated by the new algorithm.

Minimum norm interpolation in the ℓ1(ℕ) space

2020 ◽  
Vol 19 (01) ◽  
pp. 21-42
Author(s):  
Raymond Cheng ◽  
Yuesheng Xu
Keyword(s):  
Linear Programming ◽  
Euclidean Space ◽  
Interpolation Problem ◽  
Dual Space ◽  
Original Problem ◽  
Dual Problem ◽  
Dimensional Euclidean Space ◽  
Minimum Norm ◽  
Finite Dimensional ◽  
Sparse Interpolation

We consider the minimum norm interpolation problem in the [Formula: see text] space, aiming at constructing a sparse interpolation solution. The original problem is reformulated in the pre-dual space, thereby inducing a norm in a related finite-dimensional Euclidean space. The dual problem is then transformed into a linear programming problem, which can be solved by existing methods. With that done, the original interpolation problem is reduced by solving an elementary finite-dimensional linear algebra equation. A specific example is presented to illustrate the proposed method, in which a sparse solution in the [Formula: see text] space is compared to the dense solution in the [Formula: see text] space. This example shows that a solution of the minimum norm interpolation problem in the [Formula: see text] space is indeed sparse, while that of the minimum norm interpolation problem in the [Formula: see text] space is not.

scholarly journals A Novel Lagrangian Multiplier Update Algorithm for Short-Term Hydro-Thermal Coordination

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6621
Author(s):  
P. M. R. Bento ◽  
S. J. P. S. Mariano ◽  
M. R. A. Calado ◽  
L. A. F. M. Ferreira
Keyword(s):  
Lagrange Multipliers ◽  
Large Scale ◽  
Dual Problem ◽  
Electrical Power ◽  
Decomposition Methods ◽  
Subgradient Methods ◽  
Lagrangian Multiplier ◽  
Step Size ◽  
Primal Problem ◽  
Require Time

The backbone of a conventional electrical power generation system relies on hydro-thermal coordination. Due to its intrinsic complex, large-scale and constrained nature, the feasibility of a direct approach is reduced. With this limitation in mind, decomposition methods, particularly Lagrangian relaxation, constitutes a consolidated choice to “simplify” the problem. Thus, translating a relaxed problem approach indirectly leads to solutions of the primal problem. In turn, the dual problem is solved iteratively, and Lagrange multipliers are updated between each iteration using subgradient methods. However, this class of methods presents a set of sensitive aspects that often require time-consuming tuning tasks or to rely on the dispatchers’ own expertise and experience. Hence, to tackle these shortcomings, a novel Lagrangian multiplier update adaptative algorithm is proposed, with the aim of automatically adjust the step-size used to update Lagrange multipliers, therefore avoiding the need to pre-select a set of parameters. A results comparison is made against two traditionally employed step-size update heuristics, using a real hydrothermal scenario derived from the Portuguese power system. The proposed adaptive algorithm managed to obtain improved performances in terms of the dual problem, thereby reducing the duality gap with the optimal primal problem.

Sign in / Sign up

Export Citation Format

Share Document