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[docs] class abs(Elementwise): """ Elementwise absolute value """ _allow_complex = True def __init__(self, x) -> None: super(abs, self).__init__(x) # Returns the elementwise absolute value of x. @Elementwise.numpy_numeric def numeric(self, values): return np.absolute(values[0]) def sign_from_args(self) -> Tuple[bool, bool]: """Returns sign (is positive, is negative) of the expression.

Stack Overflow

stackoverflow.com › questions › 67350713 › absolute-value-function-not-recognized-as-disciplined-convex-program-cvxpy

python 3.x - Absolute value function not recognized as Disciplined Convex Program (CVXPY) - Stack Overflow

import cvxpy as cp import numpy ... DCP. Its following subexpressions are not: param516 @ abs(var513 + -param515) The absolute function is convex....

Cvxpy

cvxpy.org › tutorial › functions › index.html

Atomic Functions -

The nuclear norm can also be defined ... singular values. The functions max and min give the largest and smallest entry, respectively, in a single expression. These functions should not be confused with maximum and minimum (see Elementwise functions). Use maximum and minimum to find the max or min of a list of scalar expressions. The CVXPY function sum ...

Cvxr

ask.cvxr.com › t › absolute-value-function-used-in-the-objective-of-disciplined-convex-program-cvxpy › 8699

Absolute value function used in the objective of Disciplined Convex Program (CVXPY) - CVX Forum: a community-driven support forum

May 1, 2021 - I am trying to run the following optimization using CVXPY: weights_vec = cp.Variable(10) er_vec = cp.Parameter(10, value=np.random.randn(10)) prev_h_vec = cp.Parameter(10, value=np.ones(10)) tcost_vec = cp.Parameter(10, value=[0.03]*10) objective = cp.Maximize(weights_vec @ er_vec - tcost_vec @ cp.abs(weights_vec - prev_h_vec)) prob = cp.Problem(objective) prob.solve() However, I get the following error: cvxpy.error.DCPError: Problem does not follow DCP rules.

Snyk

snyk.io › advisor › cvxpy › functions › cvxpy.abs

How to use the cvxpy.abs function in cvxpy | Snyk

start_period: pd.Period, end_period: pd.Period, currency: Currency) -> EfficientFrontier: asset_rors = [a.get_return().values for a in assets] mu = np.mean(asset_rors, axis=1) sigma = np.cov(asset_rors) efficient_frontier = EfficientFrontier(start_period=start_period, end_period=end_period, currency=currency) for idx, return_trg in enumerate(np.linspace(mu.min(), mu.max(), num=samples_count)): w = cvx.Variable(len(assets)) ret = mu.T * w risk = cvx.quad_form(w, sigma) problem = cvx.Problem(objective=cvx.Minimize(risk), constraints=[cvx.sum(w) == 1, w >= 0, cvx.abs(ret - return_trg) <=

Cvxpy

cvxpy.org › api_reference › cvxpy.atoms.elementwise.html

cvxpy.atoms.elementwise package -

class cvxpy.abs(x)[source]¶ · Bases: Elementwise · Elementwise absolute value · class cvxpy.entr(x)[source]¶ · Bases: Elementwise · Elementwise $-x\log x$. class cvxpy.exp(x)[source]¶ · Bases: Elementwise · Elementwise $e^{x}$. class cvxpy.huber(x, M: int = 1)[source]¶ ·

Readthedocs

cvxpy.readthedocs.io › en › latest › _modules › cvxpy › atoms › elementwise › abs.html

cvxpy.atoms.elementwise.abs -

Cvxpy

cvxpy.org › version › 1.3 › _modules › cvxpy › atoms › elementwise › abs.html

cvxpy.atoms.elementwise.abs — CVXPY 1.3 documentation

[docs]class abs(Elementwise): """ Elementwise absolute value """ _allow_complex = True def __init__(self, x) -> None: super(abs, self).__init__(x) # Returns the elementwise absolute value of x. @Elementwise.numpy_numeric def numeric(self, values): return np.absolute(values[0]) def sign_from_args(self) -> Tuple[bool, bool]: """Returns sign (is positive, is negative) of the expression.

Stack Exchange

or.stackexchange.com › questions › 12197 › penalize-absolute-value-while-keeping-the-problem-dpp-cvxpy

disciplined convex programming - Penalize absolute value while keeping the problem DPP (CVXPY) - Operations Research Stack Exchange

Top answer

1 of 1

The solution occurred to me as I was writing this... In case helpful, here it is:

import cvxpy as cp
n_vars = 3
x = cp.Variable(shape=(n_vars,))
c = cp.Parameter(shape=(n_vars,), nonpos=True)
a = cp.Parameter(shape=(n_vars,))
g = cp.Parameter(shape=(n_vars,))
g_var = cp.Variable(shape=(n_vars,))
penalization_term = c @ cp.abs(x - g_var)
prob = cp.Problem(cp.Maximize(a @ x + penalization_term), [g_var == g])
print(f"Penalization term is: 1) DCP: {penalization_term.is_dcp()} 2) DPP: {penalization_term.is_dcp(dpp=True)}")
print(f"Problem is: 1) DCP: {prob.is_dcp()}, 2) DPP: {prob.is_dcp(dpp=True)}")

Google Groups

groups.google.com › g › cvxpy › c › c0CmNleJwTs

Linear formulation of an absolute value constraint used to limit portfolio turnover

November 30, 2016 - to cvxpy · I have two types of absolute value constraints · abs(Xi) <= U · and · sum[abs(Xi)] <= U · which represent the cases where X= w-w0 , w0 denotes the current portfolio weights and w the targeted portfolio weights, so that the amount to be traded is x= w - w0.

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Cvxopt

cvxopt.org › userguide › modeling.html

Modeling — CVXOPT User's Guide

A more general Python convex modeling package is CVXPY. Optimization variables are represented by variable objects. ... A vector variable. The first argument is the dimension of the vector (a positive integer with default value 1). The second argument is a string with a name for the variable.

Cvxpy

cvxpy.org › version › 1.1 › api_reference › cvxpy.atoms.elementwise.html

cvxpy.atoms.elementwise package — CVXPY 1.1.24 documentation

All of the atoms listed here operate elementwise on expressions. For example, exp exponentiates each entry of expressions that are supplied to it · Elementwise absolute value

Cvxpy

cvxpy.org › api_reference › cvxpy.transforms.html

Transforms -

to define the entrywise absolute value of x. Parameters:¶ · prob : Problem · The problem to partially optimize. opt_vars : list, optional¶ · The variables to optimize over. dont_opt_vars : list, optional¶ · The variables to not optimize over. solver : str, optional¶ ·

ProgramCreek

programcreek.com › python › example › 114235 › cvxpy.abs

Python Examples of cvxpy.abs

def mixing_sp(y_fit,ref1,ref2): ... ----- Performs the calculation by minimizing the sum of the least absolute value of the objective function: obj = sum(abs(y_fit-(ref1*F1 + ref2*(1-F1)))) Uses cvxpy to perform this calculation """ try: import cvxpy except ImportError: ...

Stack Exchange

or.stackexchange.com › questions › 7772 › adding-cvxpy-abs-to-optimization-problem-turns-out-to-be-non-dcp

Adding CVXPY abs to optimization problem turns out to be non-DCP - Operations Research Stack Exchange

Top answer

1 of 1

I think you want cp.norm1(beta - s), with no need for abs. This is DCP-compliant.

Taking separate norms of beta and s doesn't make sense for what you describe.

Edit: Note that by using norm1, you are minimizing L1 distance. That's fine if that's what you really want. However, distance, when not otherwise qualified, more commonly means L2 (Euclidean) distance, for which you would use norm (with default p) or norm2.

Ee227c

ee227c.github.io › code › lecture22.html

Non-convex constraints part 2: projected gradient descent

Projected gradient descent for sparse vectors is also known as Iterative Hard Thresholding (IHT) because the projection step (find the closest $s$-sparse vector) corresponds to hard thresholding the vector. In particular, we keep only the $s$ largest entries (in absolute value) and set the rest to 0.

Stack Overflow

stackoverflow.com › questions › 55607932 › cvxpy-minimizecvx-sumcvx-absdo-somthing-problem-solver-return-a-negat

cvxpy.Minimize(cvx.sum([cvx.abs(do somthing)...])) Problem.solver return a negative value - Stack Overflow

April 10, 2019 - obj_func.append(cvx.abs(total_tar + cvx.sum(con_expr_neg))) obj = cvx.Minimize(cvx.sum(obj_func)) prob = cvx.Problem(obj, constraints) prob.solve(solver=solver) print('result status：' + str(prob.status)) print('minimize value：', prob.value) result status：optimal minimize value： -1.886588863285761e-06 this my recalculat use common alg： 4.268472548574209e-05 ... Both look like zero to me. One cannot expect miracles from floats. – Rodrigo de Azevedo Commented Apr 11, 2019 at 5:44 · yes, its look like zero, I want why its is negative, rather than positive? Is it because float overflow or cvxpy.abs's algorithm?

Cvxr

ask.cvxr.com › nonconvex

How to Define the Absolute function in the Constraint of SDP - Nonconvex - CVX Forum: a community-driven support forum

November 15, 2022 - Hi folks, As you can see the fourth constraint has an absolute value. When I use abs(.) function, CVX makes an error: Error using cvxprob/newcnstr Disciplined convex programming error: Invalid constraint: {real affine}

Cvxpy

cvxpy.org › api_reference › cvxpy.constraints.html

Constraints -

The absolute tolerance to impose on the violation. ... True if the violation is less than tolerance, False otherwise. ... ValueError – If the constrained expression does not have a value associated with it.

GitHub

github.com › cvxpy › cvxpy › discussions › 1642

Adding CVXPY abs to optimization problem turns out to be non-DCP · cvxpy/cvxpy · Discussion #1642

I have tried to solve an optimization problem using CVXPY library. Basically, this problem is regression with additional regularizer, where sv is n-dimensional vector containing additional information used to define this regularizer. This problem aims to minimize the loss function and distance between beta, which can be positive or negative real numbers, and sv containing only positive real number, as follows · beta = cp.Variable(n) s = cp.Parameter(len(n), nonneg=False, value=sv) problem = cp.Problem(cp.Minimize((cp.sum_squares(X @ beta - Y)) + (lambda1 * cp.abs(cp.norm1(beta) - cp.norm1(s)))))

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